Modified Depth-first Search Research Articles

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design.

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here, we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library, ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. We also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

PERBANDINGAN KEEFEKTIFAN ALGORITMA BACKTRACKING DAN SOFT COMPUTING DALAM MEMECAHKAN PERMAINAN PAPAN NONOGRAM

Solving logic puzzles using specific algorithms presents an intriguing challenge where the efficiency of the approaches is crucial. One such puzzle involves solving nonograms, where the task is to fill in board fields according to the conditions specified for each row and column. The availability of various methods allows for comparing their efficiency and effectiveness. This study aimed to evaluate the effectiveness of two algorithms from different categories. We selected a modified Depth-First Search (DFS) method and a soft computing method based on permutations generation to solve a set of chosen nonograms. The research was conducted using four different board sizes, and the results indicated that the effectiveness of the methods largely depends on the complexity of the nonogram. The algorithm employing permutations consistently produced stable results, while the DFS method did not always guarantee a complete solution.

Method of Modified Depth-First Search in a Graph for Constructing All Possible Gray Codes of a Specified Length

Method of Modified Depth-First Search in a Graph for Constructing All Possible Gray Codes of a Specified Length

Finding all minimum cost flows and a faster algorithm for the K best flow problem

This paper addresses the problem of determining all optimal integer solutions of a linear integer network flow problem, which we call the all optimal integer flow (AOF) problem. We derive an O ( F ( m + n ) + m n + M ) time algorithm to determine all F many optimal integer flows in a directed network with n nodes and m arcs, where M is the best time needed to find one minimum cost flow. We remark that stopping Hamacher’s well-known method for the determination of the K best integer flows (Hamacher, 1995) at the first sub-optimal flow results in an algorithm with a running time of O ( F m ( n log n + m ) + M ) for solving the AOF problem. Our improvement is essentially made possible by replacing the shortest path sub-problem with a more efficient way to determine a so-called proper zero cost cycle using a modified depth-first search technique. As a byproduct, our analysis yields an enhanced algorithm to determine the K best integer flows that runs in O ( K n 3 + M ) . Besides, we give lower and upper bounds for the number of all optimal integer and feasible integer solutions. Our bounds are based on the fact that any optimal solution can be obtained by an initial optimal tree solution plus a conical combination of incidence vectors of all induced cycles with bounded coefficients.

Population Synthesis by Disaggregating OD Matrices with Time-Progressive Graphs for Agent-based Simulations

In the last decades, a shift from traditional travel demand modeling approaches to more fine-granular agent-based simulations occurred. This transition was supported by technological advances in computation capabilities and research regarding the capture of individual travel behaviors. While agent-based simulations have been well established, Origin-Destination (OD) matrices are still widely adopted, especially since much historical data is available in these formats. Furthermore, defining models with OD matrices requires less input than detailed microsimulation models. OD matrix-based modeling frameworks may be adequate for mostly car-and public transit-oriented traffic models; however, OD matrices fail to capture the interaction between different trips and do not regard the traveler as a central entity for travel decisions. These limits become especially apparent when regarding intermodal trips. Hence, we want to bridge the gap between traditional OD matrix-based travel demand analysis and modern agent-based simulations. We propose to compute realistic activity schedules from OD matrices that we refine with external statistics. The algorithm converts OD matrices into a graph, where each path represents a valid activity schedule. With a modified depth-first search, we extract activity schedules from the graph and assign them to agents. The practicability of the algorithm is demonstrated by a runtime analysis and a case study. The results show that the algorithm computes activity schedules while keeping the original flow from the OD matrices.

Algorithms Effectiveness comparison in solving Nonogram boards

Solving logic puzzles using selected algorithms is an interesting problem in which the efficiency of the methods is essential. One such task is solving nonograms, where board fields should be filled to meet the conditions for rows and columns, respectively. The number of methods possible to use opens the possibility to compare their efficiency and quality of operation.In this paper, we decided to analyze two algorithms belonging to different groups of methods to determine their effectiveness. A modified Depth-First Search (DFS) method and a soft computing method based on permutations generation were chosen and used to solve selected nonograms. The research was conducted on four board sizes, and the results showed that the effectiveness of the used methods mainly depends on the level of nonogram complexity. The algorithm results with permutations were stable, while in contrast to the DFS method, it did not always guarantee a complete solution.

Determination of optimal breakpoint set of overcurrent relays using modified depth‐first search and mixed‐integer linear programming

In the process of determination of breakpoints for overcurrent relays (OCR), different sets with equal number of OCRs can be selected as minimum breakpoint set (MBPS). Therefore, determination of the most appropriate MBPS is an issue which must be considered. This paper proposes a prioritization between different MBPSs based on the sum of operating times of OCRs. In this case, a set which results the minimum sum of operating times is selected as the appropriate MBPS. For this purpose, the proposed method combines two problems: determination of MBPS and coordination of OCRs, and is expressed in mixed-integer linear programming (MILP) form. Also, a modified depth-first search (DFS) algorithm is applied to determine the OCRs contained loops of studied networks. It is shown that the proposed method has the capability to be combined with previous defined expert rules in this field in order to consider the network conditions in the process of determination of optimal breakpoint set (OBPS). The proposed method has been implemented on various size networks, and the results show the effectiveness of proposed method in determining breakpoint set with the least number in first priority and minimum operating times of relays in second priority.

N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia.

Objective:We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia.Methods:It uses a source-based morphometry approach (i.e., independent component analysis (ICA) of gray matter segmentation maps) to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search (DFS) technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa.Results:Findings demonstrate multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia respectively.Conclusion:N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications.Significance:The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

Multi-keyword synonym search over encrypted cloud data using classified category-dictionary and BMIS tree

Due to the emerging popularity of cloud computing (CC), the individuals and organisations are driven to outsource their private data onto the cloud service provider (CSP) in order to achieve less maintenance cost, great flexibility, and ease of access. The data normally encrypt before outsourced to the CSP, which obsolete plain-text searching techniques over encrypted cloud-data. Thus, this paper presents an efficient and accurate multi-keyword synonym ranked search method over encrypted cloud data (MKSRSE). The objectives of MKSRSE are as follows: 1) to build a balanced m-way index search (BMIS) tree; 2) propose a modified depth first search (MDFS) technique to search for the top score ranked documents; 3) propose a classified category-dictionary; 4) propose a methodology to extract keywords from each category documents to form the category sub-dictionary. An extensive research shows that the proposed mechanisms provide better search efficiency and accuracy in comparison with other existing techniques.

Efficient indexing structure to handle durable queries through web crawling

This paper studies efficient processing of durable top-k queries on historical time series databases. Durable top-k queries, obtained as an extension of snapshot top-k queries during a certain time period, play a key role in finding objects with durable quality and predicting the status of these objects for successive time intervals by updating the query interval at all timestamps. Web crawling and indexing are tremendously significant in recent times, especially in terms of achieving efficient durable top-k queries from vast quantum of web documents. Existing algorithms that have been employed throw up results that are less than applicable to analyzers. This paper chiefly focuses on web crawling and indexing query terms under their respective categories and updating rank changes at every time interval. Links are crawled using the modified depth-first search (MDFS) algorithm, accessed, and metadata such as the title, keywords, and descriptions extracted. To handle query indexing, novel indexing techniques are proposed to yield efficient results. This study is invaluable for analysts working on large data obtained as a result of crawling and indexing, effectively decreasing their workload.

Efficiently identifying critical nodes in large complex networks

The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, such that the residual graph has minimum pairwise connectivity for user-defined value k. Existing optimization algorithms are incapable of finding a good set R in graphs with many thousands or millions of vertices due to the associated computational cost. Hence, there exists a need for a time- and space-efficient approach for evaluating the impact of removing any v∈V in the context of the CNDP. In this paper, we propose an algorithm based on a modified depth-first search that requires O(k(|V|+|E|)) time complexity. We employ the method within in a greedy algorithm for quickly identifying R. Our experimental results consider small- (≤250 nodes) and medium-sized (≤25,000 nodes) networks, where it is possible to compare to known optimal solutions or results obtained by other heuristics. Additionally, we show results using six real-world networks. The proposed algorithm can be easily extended to vertex- and edge-weighted variants of the CNDP.

Coverage path planning for UAVs in unknown directional regions

This paper addresses the problem of path planning for Unmanned Aerial Vehicles UAVs in an unknown directional region. Paths are planned to maximise the amount of information from desired region while accounting for the trajectory length. In this paper, an exploration system is proposed for specific cases with shifting directions such as rivers and lakes. Taking account of data synchronisation errors, this method maximises the amount of information value and optimises the path length. Desired region is divided into grids based on required image resolution as well as time needed during the process. Analyses show that the real area and path length are closely related to the yaw angle of UAVs. The path planning issue is studied as an optimisation problem and has been solved by a modified depth-first search method. Monte-Carlo simulations are carried out to validate the effectiveness of the proposed algorithm, in which the UAV performs a task to track targets on a flowing river.

A C++ object-oriented toolkit for track finding with k-dimensional hits

A library is described for the recognition of tracks in a set of hits. The hits are assumed to be k-dimensional points ( k- d), with k ≥ 1 , of which a subset can be grouped into tracks by using short-range correlations. A connection graph between the hits is created by sorting the hits first in k- d space using one of the developed, fast, k-space containers. The track-finding algorithm considers any connection between two hits as a possible track seed and grows these seeds into longer track segments using a modified depth-first search of the connection graph. All hit-acceptance decisions are called via abstract methods of an acceptance criterion class which isolates the library from the application's hit and track model. An application is tuned for a particular tracking environment by creating a concrete implementation for the hit and track acceptance calculations. The implementer is free to trade tracking time for acceptance complexity (influencing efficiency) depending on the requirements of the particular application. Results for simulated data show that the track finding is both efficient and fast even for high noise environments.

Delay-time modelling and critical-path verification for cmos digital designs

A new switch-level critical-path verifier for cmos digital circuits is presented. The signal flow through mos transistors is determined by combining the designer's tag with a set of direction-derivation rules, and the delays are evaluated on a stage-by-stage basis. The paper uses a semianalytic cmos delay-time model that takes into account the configuration ratio, the input waveform slope and the load condition. This model is derived from the optimally weighted switching peak current, which is, in turn, derived from the Shichman-Hodges' DC equations. The delay equations are computationally effective, and the error is found to be typically within 10% of the spice results. A novel methodology for reporting multiple paths, called a modified depth-first search with predictor, is investigated. In comparison with the conventional method without predictor, it requires much less CPU time.