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Stabilization of Charge-Transfer Excited States in Biological Systems: A Computational Focus on the Special Pair in Photosystem II Reaction Centers
No full textCharge-transfer (CT) excited states play an important role in many biological processes. However, many computational approaches often inadequately address the equilibration effects of nuclear and environmental degrees of freedom on these states. One prominent example of systems in which CT states are of utmost importance is reaction centers (RC) in photosystems. Here we use a multiscale approach combined with time-dependent density functional theory to explore the lowest CT excited state of the special pair PD1–PD2 in the Photosystem II-RC of a cyanobacterium. We find that the nonequilibrium CT excited state resides near the Soret band, making an exciton the lowest-energy excited state. However, accounting for nuclear and state-specific dielectric equilibration along the CT potential energy surface (PES), the CT state PD1––PD2+ stabilizes energetically below the excitonic state. This underscores the crucial role of state-specific solvation in mapping the PES of CT states, as demonstrated in a simplified dimer model.Fil: Forde, Aaron. Los Alamos National Laboratory; Estados UnidosFil: Maity, Sayan. Constructor University; AlemaniaFil: Freixas, Victor M.. University of California at Irvine; Estados UnidosFil: Fernández Alberti, Sebastián. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Area Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Neukirch, Amanda J.. Los Alamos National Laboratory; Estados UnidosFil: Kleinekathöfer, Ulrich. Constructor University; AlemaniaFil: Tretiak, Sergei. Los Alamos National Laboratory; Estados Unido
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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3D Ear Biometrics and Surveillance Video Based Biometrics.
No full textIn the current era of Digital Information Technology, Biometrics authentication is massively used to protect user's privacy by confirming the legitimacy of their identity. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Technology to verify a person’s identity based on his/her biometrics utilizes the information "someone who they are" instead of "something they know" (passwords) or "something they possess"(ID card). Biometric identifiers are usually categorized as physiological and behavioral characteristics. Physiological characteristics are related to the shape of the body. Examples include, but are not limited to face recognition, ear shape, fingerprint, DNA, palm print, hand geometry, iris recognition, and retina. Behavioral characteristics refer to the pattern of behavior of a person, including but not limited to typing rhythm, gait, and voice. Application of Biometrics authentication can be found in various domains, starting from forensics research, border security maintenance to securely access Bank ATMs and the web or mobile applications. Current growth trends in different biometrics applications present challenges to researchers. To address these challenges, we need new data storage and retrieval techniques to make the recognition process time efficient. We proposed a system for time efficient 3D ear biometrics from a large biometrics database. The proposed system has two components that are primarily responsible for: 1) automatic 3D ear segmentation and 2) hierarchical categorization of the 3D ear database using shape information and surface depth information, respectively. We use an active contour algorithm along with a tree structured graph to segment the ear region from the 3D profile images. The segmented 3D ear database is then categorized based on geometrical feature values, computed from the ear shape, into oval, round, rectangular and triangular categories. For the categorization based on the depth information, the feature space is partitioned using tree-based indexing techniques. We used indexing techniques with balanced split (KD tree) and unbalanced split (Pyramid tree) data structures to categorize the database separately, then compared their retrieval efficiency. Experiments are conducted to compare the average computation time per query when performing recognition through hierarchical categorization with the average computation time when recognition is based on sequential search. Experimental results conducted on the University of Notre Dame (UND) collection J2 dataset demonstrate that the proposed approach outperforms state-of-the-art 3D ear biometric systems in both accuracy and efficiency, explicitly the hierarchical clustering of the biometrics dataset result in 5 times faster search/ query compared with the state-of-the-art technique that uses sequential search. Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. We proposed a novel multimodal recognition system that trains a Deep Learning Network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising autoencoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. The proposed system has three components that are responsible for: 1) Automatically detecting images of different modalities present in the facial video clips; 2) Training supervised denoising sparse autoencoders to capture the modality specific discriminative representation while maintaining robustness to the variations; and 3) Train modality specific Sparse classifier (SRC), then perform score level fusion of the recognition results of all five modalities, or all the available modalities from the query video to obtain the multimodal recognition result. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips. Biometric identification using Surveillance Video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. We present a novel multimodal recognition system that extracts Frontal Gait and Low Resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the Model-Free and Model-Based Gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing Frontal Gait recognition and one is responsible for Low Resolution face recognition. Later, score level fusion is performed to fuse the results of the Frontal Gait recognition and the Low Resolution Face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for Frontal Gait recognition and 82.92% Rank-1 for Low Resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.</p
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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