Search results for: ontology V-alignment pair

Authors: A. Ferasat, S. Rostampour Yasouri

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Seasonal flu is a highly contagious infection caused by influenza viruses. These viruses undergo genetic changes that result in new epidemics across the globe. Medical attention is crucial in severe cases, particularly for the elderly, frail, and those with chronic illnesses, as their immune systems are often weaker. The purpose of this study was to detect new subtypes of the influenza A virus rapidly using a specific RT-PCR method based on the HA gene (hemagglutinin). In the winter and spring of 2022_2023, 120 embryonated egg samples were cultured, suspected of seasonal influenza. RNA synthesis, followed by cDNA synthesis, was performed. Finally, the PCR technique was applied using a pair of specific primers designed based on the HA gene. The PCR product was identified after purification, and the nucleotide sequence of purified PCR products was compared with the sequences in the gene bank. The results showed a high similarity between the sequence of the positive samples isolated from the patients and the sequence of the new strains isolated in recent years. This RT-PCR technique is entirely specific in this study, enabling the detection and multiplication of influenza and its subspecies from clinical samples. The RT-PCR technique based on the HA gene, along with sequencing, is a fast, specific, and sensitive diagnostic method for those infected with influenza viruses and its new subtypes. Rapid molecular diagnosis of influenza is essential for suspected people to control and prevent the spread of the disease to others. It also prevents the occurrence of secondary (sometimes fatal) pneumonia that results from influenza and pathogenic bacteria. The critical role of rapid diagnosis of new strains of influenza is to prepare a drug vaccine against the latest viruses that did not exist in the community last year and are entirely new viruses.

Keywords: influenza, molecular diagnosis, patients, RT-PCR technique

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Authors: Sisay Shine, Frew Tadesse, Zemenu Shiferaw, Lema Mideksa

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Background: Stunting is one of the most important public health problems in Ethiopia with an estimated 44.4% of children less than five years of age are stunted. Thus, this study aimed to assess prevalence and associated factors of stunting among 6-59 months children in pastoral community of Korahay Zone, Somali Regional State, Ethiopia. Objective of the study: To assess prevalence and associated factors of stunting among 6-59 months children in pastoral community of Korahay Zone, Somali Regional State, Ethiopia, 2016. Methods: Community based cross sectional study design was done among 770 children in pastoral community of Korahay Zone. Systematic sampling techniques were used to select households and took child mother pair from each selected households. Data was collected using pre-tested and structured questionnaire. Odds ratio with 95% confidence interval was used to assess level of significance. Result: Prevalence of stunting among 6-59 months age children was 31.9%. Sex (AOR: 1.47, 95%CI 1.02, 2.11), age (AOR: 2.10, 95%CI 1.16, 3.80), maternal education (AOR: 3.42, 95%CI 1.58, 7.41), maternal occupation (AOR: 3.10, 95%CI 1.85, 5.19), monthly income (AOR: 1.47, 95%CI 1.03, 2.09), PNC visits (AOR: 1.59, 95%CI 1.07, 2.37), source of water (AOR: 3.41, 95%CI 1.96, 5.93), toilet availability (AOR: 1.71, 95%CI 1.13, 2.58), first milk feeding (AOR: 3.37, 95%CI 2.27, 5.02) and bottle feeding (AOR: 2.07, 95%CI 1.34, 3.18) were significant predictors of stunting. Conclusion and recommendations: Prevalence of stunting among 6-59 months children was high 31.9%. Lack maternal education, not feeding first milk, unsafe water supply, absence toilet availability and bottle feeding can increase the risk of stunting. So, educating mothers on child feeding practice, sanitation and important of first milk can reduce stunting.

Keywords: dietary, environmental, healthcare, socio-demographic, stunting

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Authors: Fai-Ho E. Choi, Wing-Tung Au

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This study investigated the effects of embodiment on conflict management. As shown in the research literature, the physiological (i.e. bodily postures) can affect the emotional and cognitive proceedings of human beings, but little has been shown on whether such effects would have ramifications in decision-making related to other individuals. In this study, conflict is defined as when two parties have seemingly incompatible goals, and the two have to deal with each other in order to maximize one’s own gain. In a matched-gender experiment, university undergraduate students were randomly assigned to either the high power condition or the low power condition, with participants in each condition instructed to perform a fix set of bodily postures that would either embody them with a high sense of power or a low sense of power. One high-power participant would pair up with a low-power participant to engage in an integrative bargaining task and a dictator game. Participants also filled out a pre-trial questionnaire and a post-trial questionnaire measuring general sense of power, self-esteem and self-efficacy. Personality was controlled for. Results are expected to support our hypotheses that people who are embodied with power will be more unyielding in a conflict management situation, and that people who are dealing with another person embodied with power will be more yielding in a conflict management situation. As conflicts arise frequently both within and between organizations, a better understanding of how human beings function in conflicts is important. This study should provide evidence that bodily postures can influence the perceived sense of power of the parties involved and hence influence the conflict outcomes. Future research needs to be conducted to investigate further how people perceive themselves and how they perceive their opponents in conflicts, such that we can come up with a behavioral theory of conflict management.

Keywords: conflict management, embodiment, negotiation, perception

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Authors: Armando Cuspinera

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Humans are nature itself, being with non-human species part of the same ecosystem, but the praxis reflects that not all relations are the same. In fields of design such as Biomimicry, Biodesign, and Biophilic design exist different approaches towards nature, nevertheless, anthropocentric principles such as domination, objectivization, or exploitation are defined in the same as ecocentric principles of inherent importance in life itself. Anthropocentrism has showed humanity with pollution of the earth, water, air, and the destruction of whole ecosystems from monocultures and rampant production of useless objects that life cannot outstand this unaware rhythm of life focused only for the human benefits. Even if by nature the biosphere is resilient, studies showed in the Paris Agreement explain that humanity will perish in an unconscious way of praxis. This is why is important to develop a differentiation between anthropocentric and ecocentricprinciples in the praxis of design, in order to enhance respect, valorization, and positive affectivity towards other life forms is necessary to analyze what principles are reproduced from each practice of design. It is only from the study of immaterial dimensions of design such as symbolism, epistemology, and ontology that the relation towards nature can be redesigned, and in order to do so, it must be studies from the dimensions of ontological design what principles –anthropocentric or ecocentric- through what the objects enhance or focus the perception humans have to its surrounding. The things we design also design us is the principle of ontological design, and in order to develop a way of ecological design in which is possible to consider other species as users, designers or collaborators is important to extend the studies and relation to other living forms from a transdisciplinary perspective of techniques, knowledge, practice, and disciplines in general. Materials, technologies, and any kind of knowledge have the principle of a tool: is not good nor bad, but is in the way of using it the possibilities that exist within them. The collaboration of disciplines and fields of study gives the opportunity to connect principles from other cultures such as Deep Ecology and Environmental Humanities in the development of methodologies of design that study nature, integrates their strategies to our own species, and considers life of other species as important as human life, and is only form the studies of ontological design that material and immaterial dimensions can be analyzed and imbued with structures that already exist in other fields.

Keywords: design, antropocentrism, ecocentrism, ontological design

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Authors: Mohamed Osama Hassaan

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Recent earthquakes have demonstrated the vulnerability of older reinforced concrete buildings to fail under imposed seismic loads. Accordingly, the need to strengthen existing reinforced concrete structures, mainly columns, to resist high seismic loads has increased. Conventional strengthening techniques such as using steel plates, steel angles and concrete overlay are used to achieve the required increase in strength or ductility. However, techniques using advanced composite materials are established. The column's splice zone is the most critical zone that failed under seismic loads. There are three types of splice zone failure that can be observed under seismic action, namely, Failure of the flexural plastic hinge region, shear failure and failure due to short lap splice. A lapped splice transfers the force from one bar to another through the concrete surrounding both bars. At any point along the splice, force is transferred from one bar by a bond to the surrounding concrete and also by a bond to the other bar of the pair forming the splice. The integrity of the lap splice depends on the development of adequate bond length. The R.C. columns built in seismic regions are expected to undergo a large number of inelastic deformation cycles while maintaining the overall strength and stability of the structure. This can be ensured by proper confinement of the concrete core. The last type of failure is focused in this research. There are insufficient studies that address the problem of strengthening existing reinforced concrete columns at splice zone through confinement with “advanced composite materials". Accordingly, more investigation regarding the seismic behavior of strengthened reinforced concrete columns using the new generation of composite materials such as (Carbon fiber polymer), (Glass fiber polymer), (Armiad fiber polymer).

Keywords: strengthening, columns, advanced composite materials, earthquakes

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Authors: Siddhi Khaire, Samarth Hawaldar, Baladitya Suri

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As efforts towards quantum communication advance, the need for single photon sources becomes imminent. Due to the extremely low energy of a single microwave photon, efforts to build single photon sources and detectors in the microwave range are relatively recent. We plan to use a Cooper Pair Box (CPB) that has a ‘sweet-spot’ where the two energy levels have minimal separation. Moreover, these qubits have fairly large anharmonicity making them close to ideal two-level systems. If the external gate voltage of these qubits is varied rapidly while passing through the sweet-spot, due to Landau-Zener effect, the qubit can be excited almost deterministically. The rapid change of the gate control voltage through the sweet spot induces a non-adiabatic population transfer from the ground to the excited state. The qubit eventually decays into the emission line emitting a single photon. The advantage of this setup is that the qubit can be excited without any coherent microwave excitation, thereby effectively increasing the usable source efficiency due to the absence of control pulse microwave photons. Since the probability of a Landau-Zener transition can be made almost close to unity by the appropriate design of parameters, this source behaves as an on-demand source of single microwave photons. The large anharmonicity of the CPB also ensures that only one excited state is involved in the transition and multiple photon output is highly improbable. Such a system has so far not been implemented and would find many applications in the areas of quantum optics, quantum computation as well as quantum communication.

Keywords: quantum computing, quantum communication, quantum optics, superconducting qubits, flux qubit, charge qubit, microwave single photon source, quantum information processing

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Authors: Joanna Maj

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Body machine interfaces (BMIs) afford users a non-invasive way coordinate movement. Vibrotactile stimulation has been incorporated into BMIs to allow feedback in real-time and guide movement control to benefit patients with cognitive deficits, such as stroke survivors. To advance research in this area, we examined vibrational discrimination thresholds at four body locations to determine suitable application sites for future multi-channel BMIs using vibration cues to guide movement planning and control. Twelve healthy adults had a pair of small vibrators (tactors) affixed to the skin at each location: forearm, shoulders, torso, and knee. A "standard" stimulus (186 Hz; 750 ms) and "probe" stimuli (11 levels ranging from 100 Hz to 235 Hz; 750 ms) were delivered. Probe and test stimulus pairs could occur sequentially or simultaneously (timing). Participants verbally indicated which stimulus felt more intense. Stimulus order was counterbalanced across tactors and body locations. Probabilities that probe stimuli felt more intense than the standard stimulus were computed and fit with a cumulative Gaussian function; the discrimination threshold was defined as one standard deviation of the underlying distribution. Threshold magnitudes depended on stimulus timing and location. Discrimination thresholds were better for stimuli applied sequentially vs. simultaneously at the torso as well as the knee. Thresholds were small (better) and relatively insensitive to timing differences for vibrations applied at the shoulder. BMI applications requiring multiple channels of simultaneous vibrotactile stimulation should therefore consider the shoulder as a deployment site for a vibrotactile BMI interface.

Keywords: electromyography, electromyogram, neuromuscular disorders, biomedical instrumentation, controls engineering

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: M. Irfan Fareed, Muhammad Tahir, Alvina Gul Kazi

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Castor bean (Ricinus communis; family Euphorbiaceae) is cultivated for the production of oil and as an ornamental plant throughout tropical regions. Leaf samples from castor bean plants with leaf curl and vein thickening were collected from areas around Okara (Pakistan) in 2011. PCR amplification using diagnostic primers showed the presence of a begomovirus and subsequently the specific pair (BurNF 5’- CCATGGTTGTGGCAGTTGATTGACAGATAC-3’, BurNR 5’- CCATGGATTCACGCACAGGGGAACCC-3’) was used to amplify and clone the whole genome of the virus. The complete nucleotide sequence was determined to be 2,759 nt (accession No. HE985227). Alignments showed the highest levels of nucleotide sequence identity (98.8%) with Cotton leaf curl Burewala virus (CLCuBuV; accession No. JF416947) No. JF416947). The virus in castor beans lacks on intact C2 gene, as is typical of CLCuBuV in cotton. An amplification product of ca. 1.4 kb was obtained in PCR with primers for betasatellites and the complete nucleotide sequence of a clone was determined to be 1373 nt (HE985228). The sequence showed 96.3% nucleotide sequence identity to the recombinant Cotton leaf curl Multan betasatellite (CLCuMB; JF502389). This is the first report of CLCuBuV and its betasatellite infecting castor bean, showing this plant species as an alternate host of the virus. Already many alternate host have been reported from different alternate host like tobacco, tomato, hibiscus, okra, ageratum, Digera arvensis, habiscus, Papaya and now in Ricinus communis. So, it is suggested that these alternate hosts should be avoided to grow near cotton growing regions.

Keywords: Ricinus communis, begomovirus, betasatellite, agriculture

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: G. M. B. P. Abeysekara, W. A. D. C. Wijerathna

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Terminal choice from the multiple terminals region is not a simple decision and it is very complex, because shipping lines should consider on influential factors for the terminal choice at once according to their requirement. Therefore, terminal choice is a multiple criterion decision making (MCDM) situation under a specially designed decision hierarchy. Identification of perspective of shipping lines regarding terminal choice is vital important for the decision makers regarding container terminals. Thus this study is evaluated perception on main and feeder shipping lines’ regarding port of Colombo container terminals, and ranked terminals according to shipping lines preference. Analytic Hierarchy Process (AHP) model is adapted to this study, since it has features similar to the MCDM, it is weighted every influential factor by using pair wise comparisons, and consistency of the decision makers’ judgments are checked to evaluate trustworthiness of gathered data. And rating method is used to rank the terminals within Port of Colombo by assigning particular preference values with respect to the criteria and sub criteria. According to the findings of this study, main lines’ mainly concern on water depth of approach channel, depth of berth, handling charges and handling equipment facilities. And feeder lines’ main concerns were handling equipment facilities, loading and discharging efficiency, depth of berth and handling charges. Findings of the study suggested concentrating regarding the emphasized areas in order to enhance the competitiveness of terminals, and to increase number of vessel callings at the Port of Colombo. Application of above finding of the terminals within Port of Colombo lead to a far better competition among terminals and would uplift the overall level of services.

Keywords: AHP, Main and feeder shipping lines, criteria, sub criteria

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Authors: Sammy Kibor, Filip Huyghe, Marc Kochzius, James Kairo

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Goldstripe Sardinella, Sardinella gibbosa, (Bleeker, 1849) is a commercially and ecologically important small pelagic fish common in the Western Indian Ocean region. The present study aimed to assess genetic diversity and population structure of the species in the Kenya-Tanzania transboundary area using mtDNA and msDNA markers. Some 630 bp sequence in the mitochondrial DNA (mtDNA) Cytochrome C Oxidase I (COI) and five polymorphic microsatellite DNA loci were analyzed. Fin clips of 309 individuals from eight locations within the transboundary area were collected between July and December 2018. The S. gibbosa individuals from the different locations were distinguishable from one another based on the mtDNA variation, as demonstrated with a neighbor-joining tree and minimum spanning network analysis. None of the identified 22 haplotypes were shared between Kenya and Tanzania. Gene diversity per locus was relatively high (0.271-0.751), highest Fis was 0.391. The structure analysis, discriminant analysis of Principal component (DAPC) and the pair-wise (FST = 0.136 P < 0.001) values after Bonferroni correction using five microsatellite loci provided clear inference on genetic differentiation and thus evidence of population structure of S. gibbosa along the Kenya-Tanzania coast. This study shows a high level of genetic diversity and the presence of population structure (Φst =0.078 P < 0.001) resulting to the existence of four populations giving a clear indication of minimum gene flow among the population. This information has application in the designing of marine protected areas, an important tool for marine conservation.

Keywords: marine connectivity, microsatellites, population genetics, transboundary

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Authors: Achitphol Chookaew, Paramee Thongsukhsai, Patamarerk Engsontia, Narongwit Nakwan, Pritsana Raugrut

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Lung cancer is one of the most common malignancies and primary cause of death due to cancer worldwide. Non-small cell lung cancer (NSCLC) is the main subtype in which majority of patients present with advanced-stage disease. Herein, we analyzed differentially expressed genes to find potential biomarkers for lung cancer diagnosis as well as prognostic markers. We used transcriptome data from our 2 NSCLC patients and public data (GSE81089) composing of 8 NSCLC and 10 normal lung tissues. Differentially expressed genes (DEGs) between NSCLC and normal tissue and between early-stage and late-stage NSCLC were analyzed by the DESeq2. Pairwise correlation was used to find the DEGs with false discovery rate (FDR) adjusted p-value £ 0.05 and |log2 fold change| ³ 4 for NSCLC versus normal and FDR adjusted p-value £ 0.05 with |log2 fold change| ³ 2 for early versus late-stage NSCLC. Bioinformatic tools were used for functional and pathway analysis. Moreover, the top ten genes in each comparison group were verified the expression and survival analysis via GEPIA. We found 150 up-regulated and 45 down-regulated genes in NSCLC compared to normal tissues. Many immnunoglobulin-related genes e.g., IGHV4-4, IGHV5-10-1, IGHV4-31, IGHV4-61, and IGHV1-69D were significantly up-regulated. 22 genes were up-regulated, and five genes were down-regulated in late-stage compared to early-stage NSCLC. The top five DEGs genes were KRT6B, SPRR1A, KRT13, KRT6A and KRT5. Keratin 6B (KRT6B) was the most significantly increased gene in the late-stage NSCLC. From GEPIA analysis, we concluded that IGHV4-31 and IGKV1-9 might be used as diagnostic biomarkers, while KRT6B and KRT6A might be used as prognostic biomarkers. However, further clinical validation is needed.

Keywords: differentially expressed genes, early and late-stages, gene ontology, non-small cell lung cancer transcriptomics

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Authors: David C. Ni

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Contemporary models for N-body systems are based on temporal, two-body, and mass point representation of Newtonian mechanics. Other mainstream models include 2D and 3D Ising models based on local neighborhood the lattice structures. In Quantum mechanics, the theories of collective modes are for superconductivity and for the long-range quantum entanglement. However, these models are still mainly for the specific phenomena with a set of designated parameters. We are therefore motivated to develop a new construction directly from the complex-variable N-body systems based on the extended Blaschke functions (EBF), which represent a non-temporal and nonlinear extension of Lorentz transformation on the complex plane – the normalized momentum spaces. A point on the complex plane represents a normalized state of particle momentums observed from a reference frame in the theory of special relativity. There are only two key parameters, normalized momentum and nonlinearity for modelling. An algorithm similar to Jenkins-Traub method is adopted for solving EBF iteratively. Through iteration, the solution sets show a form of σ + i [-t, t], where σ and t are the real numbers, and the [-t, t] shows various distributions, such as 1-peak, 2-peak, and 3-peak etc. distributions and some of them are analog to the canonical distributions. The results of the numerical analysis demonstrate continuum-to-discreteness transitions, evolutional invariance of distributions, phase transitions with conjugate symmetry, etc., which manifest the construction as a potential candidate for the unification of statistics. We hereby classify the observed distributions on the finite convergent domains. Continuous and discrete distributions both exist and are predictable for given partitions in different regions of parameter-pair. We further compare these distributions with canonical distributions and address the impacts on the existing applications.

Keywords: blaschke, lorentz transformation, complex variables, continuous, discrete, canonical, classification

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Authors: Andrea ČopíKová

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Competency Based Management is a new approach to management, which solves organization’s challenges with complexity and with the aim to find and solve organization’s problems and learn how to avoid these in future. They teach the organizations to create, apart from the state of stability – that is temporary, vital organization, which is permanently able to utilize and profit from internal and external opportunities. The aim of this paper is to propose a process of competency model design, based on which a competency model for a financial department manager in a production company will be created. Competency models are very useful tool in many personnel processes in any organization. They are used for acquiring and selection of employees, designing training and development activities, employees’ evaluation, and they can be used as a guide for a career planning and as a tool for succession planning especially for managerial positions. When creating a competency model the method AHP (Analytic Hierarchy Process) and quantitative pair-wise comparison (Saaty’s method) will be used; these methods belong among the most used methods for the determination of weights, and it is used in the AHP procedure. The introduction part of the paper consists of the research results pertaining to the use of competency model in practice and then the issue of competency and competency models is explained. The application part describes in detail proposed methodology for the creation of competency models, based on which the competency model for the position of financial department manager in a foreign manufacturing company, will be created. In the conclusion of the paper, the final competency model will be shown for above mentioned position. The competency model divides selected competencies into three groups that are managerial, interpersonal and functional. The model describes in detail individual levels of competencies, their target value (required level) and the level of importance.

Keywords: analytic hierarchy process, competency, competency model, quantitative pairwise comparison

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Authors: Chang-Jen Lan

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Current Level of Service (LOS) index adopted in Highway Capacity Manual (HCM) for signalized intersections on surface streets is based on the intersection average delay. The delay thresholds for defining LOS grades are subjective and is unrelated to critical traffic condition. For example, an intersection delay of 80 sec per vehicle for failing LOS grade F does not necessarily correspond to the intersection capacity. Also, a specific measure of average delay may result from delay minimization, delay equality, or other meaningful optimization criteria. To that end, a reliability version of the intersection critical degree of saturation (v/c) as the LOS index is introduced. Traditionally, the level of saturation at a signalized intersection is defined as the ratio of critical volume sum (per lane) to the average saturation flow (per lane) during all available effective green time within a cycle. The critical sum is the sum of the maximal conflicting movement-pair volumes in northbound-southbound and eastbound/westbound right of ways. In this study, both movement volume and saturation flow are assumed log-normal distributions. Because, when the conditions of central limit theorem obtain, multiplication of the independent, positive random variables tends to result in a log-normal distributed outcome in the limit, the critical degree of saturation is expected to be a log-normal distribution as well. Derivation of the risk index predictive limits is complex due to the maximum and absolute value operators, as well as the ratio of random variables. A fairly accurate functional form for the predictive limit at a user-specified significant level is yielded. The predictive limit is then compared with the designated LOS thresholds for the intersection critical degree of saturation (denoted as X

Keywords: reliability analysis, level of service, intersection critical degree of saturation, risk based index

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Authors: Bryce Benson, Sooin Lee, Ashwin Belle

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Unrecognized or delayed identification of patient deterioration is a key cause of in-hospitals adverse events. Clinicians rely on vital signs monitoring to recognize patient deterioration. However, due to ever increasing nursing workloads and the manual effort required, vital signs tend to be measured and recorded intermittently, and inconsistently causing large gaps during patient monitoring. Additionally, during deterioration, the body’s autonomic nervous system activates compensatory mechanisms causing the vital signs to be lagging indicators of underlying hemodynamic decline. This study analyzes the predictive efficacy of the Analytic for Hemodynamic Instability (AHI) system, an automated tool that was designed to help clinicians in early identification of deteriorating patients. The lead time analysis in this retrospective observational study assesses how far in advance AHI predicted deterioration prior to the start of an episode of hemodynamic instability (HI) becoming evident through vital signs? Results indicate that of the 362 episodes of HI in this study, 308 episodes (85%) were correctly predicted by the AHI system with a median lead time of 57 minutes and an average of 4 hours (240.5 minutes). Of the 54 episodes not predicted, AHI detected 45 of them while the episode of HI was ongoing. Of the 9 undetected, 5 were not detected by AHI due to either missing or noisy input ECG data during the episode of HI. In total, AHI was able to either predict or detect 98.9% of all episodes of HI in this study. These results suggest that AHI could provide an additional ‘pair of eyes’ on patients, continuously filling the monitoring gaps and consequently giving the patient care team the ability to be far more proactive in patient monitoring and adverse event management.

Keywords: clinical deterioration prediction, decision support system, early warning system, hemodynamic status, physiologic monitoring

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Authors: Victor Prevost, Solene Landerneau, Michel Duhamel, Joel Sternheimer, Olivier Gallet, Pedro Ferrandiz, Marwa Mokni

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The search for homologous proteins is one of the ongoing challenges in biology and bioinformatics. Traditionally, a pair of proteins is thought to be homologous when they originate from the same ancestral protein. In such a case, their sequences share similarities, and advanced scientific research effort is spent to investigate this question. On this basis, we propose the Protein-Wave Alignment Tool (”P-WAT”) developed within the framework of the France Relance 2030 plan. Our work takes into consideration the mass-related wave aspect of protein biosynthesis, by associating specific frequencies to each amino acid according to its mass. Amino acids are then regrouped within their mass category. This way, our algorithm produces specific alignments in addition to those obtained with a common amino acid coding system. For this purpose, we develop the ”P-WAT” original algorithm, able to address large protein databases, with different attributes such as species, protein names, etc. that allow us to align user’s requests with a set of specific protein sequences. The primary intent of this algorithm is to achieve efficient alignments, in this specific conceptual frame, by minimizing execution costs and information loss. Our algorithm identifies sequence similarities by searching for matches of sub-sequences of different sizes, referred to as primers. Our algorithm relies on Boolean operations upon a dot plot matrix to identify primer amino acids common to both proteins which are likely to be part of a significant alignment of peptides. From those primers, dynamic programming-like traceback operations generate alignments and alignment scores based on an adjusted PAM250 matrix.

Keywords: protein, alignment, homologous, Genodic

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Authors: O. Onoriode Emoyan, E. Eyitemi Akporhonor, Charles Otobrise

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Polycyclic Aromatic Hydrocarbons (PAHs) are formed mainly as a result of incomplete combustion of organic materials during industrial, domestic activities or natural occurrence. Their toxicity and contamination of terrestrial and aquatic ecosystem have been established. Though with limited validity index, previous research has focused on PAHs isomer pair ratios of variable physicochemical properties in source identification. The objective of this investigation was to determine the empirical validity of Pearson correlation coefficient (PCC) and cluster analysis (CA) in PAHs source identification along soil samples of different land uses. Therefore, 16 PAHs grouped as endocrine disruption substances (EDSs) were determined in 10 sample stations in top and sub soils seasonally. PAHs was determined the use of Varian 300 gas chromatograph interfaced with flame ionization detector. Instruments and reagents used are of standard and chromatographic grades respectively. PCC and CA results showed that the classification of PAHs along kinetically and thermodyanamically-favoured and those derived directly from plants product through biologically mediated processes used in source signature is about the predominance PAHs are likely to be. Therefore the observed PAHs in the studied stations have trace quantities of the vast majority of the sixteen un-substituted PAHs which may ultimately inhabit the actual source signature authentication. Type and extent of bacterial metabolism, transformation products/substrates, and environmental factors such as: salinity, pH, oxygen concentration, nutrients, light intensity, temperature, co-substrates and environmental medium are hereby recommended as factors to be considered when evaluating possible sources of PAHs.

Keywords: comparative correlation, kinetically and thermodynamically-favored PAHs, pearson correlation coefficient, cluster analysis, sources evaluation

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Authors: Hoa Thi Hoang, Stephan Sass, Michael U. Kumke

Abstract:

Concanavalin A (conA) is a protein has been widely used in sensor system based on its specific binding to α-D-Glucose or α-D-Manose. For glucose sensor using conA, either fluoresence based techniques with intensity based or lifetime based are used. In this research, liposomes made from phospholipids were used as a biomimetic membrane system. In a first step, novel building blocks containing perylene labeled glucose units were added to the system and used to decorate the surface of the liposomes. Upon the binding between rhodamine labeled con A to the glucose units at the biomimetic membrane surface, a Förster resonance energy transfer system can be formed which combines unique fluorescence properties of perylene (e.g., high fluorescence quantum yield, no triplet formation) and its high hydrophobicity for efficient anchoring in membranes to form a novel probe for the investigation of sugar-driven binding reactions at biomimetic surfaces. Two glucose-labeled perylene derivatives were synthesized with different spacer length between the perylene and glucose unit in order to probe the binding of conA. The binding interaction was fully characterized by using high-end fluorescence techniques. Steady-state and time-resolved fluorescence techniques (e.g., fluorescence depolarization) in combination with single-molecule fluorescence spectroscopy techniques (fluorescence correlation spectroscopy, FCS) were used to monitor the interaction with conA. Base on the fluorescence depolarization, the rotational correlation times and the alteration in the diffusion coefficient (determined by FCS) the binding of the conA to the liposomes carrying the probe was studied. Moreover, single pair FRET experiments using pulsed interleaved excitation are used to characterize in detail the binding of conA to the liposome on a single molecule level avoiding averaging out effects.

Keywords: concanavalin A, FRET, sensor, biomimetic membrane

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Authors: I. Schiller, D. Morsomme, A. Remacle

Abstract:

Children’s ability to process spoken language develops until the late teenage years. At school, where efficient spoken language processing is key to academic achievement, listening conditions are often unfavorable. High background noise and poor teacher’s voice represent typical sources of interference. It can be assumed that these factors particularly affect primary school children, because their language and literacy skills are still low. While it is generally accepted that background noise and impaired voice impede spoken language processing, there is an increasing need for analyzing impacts within specific linguistic areas. Against this background, the aim of the study was to investigate the effect of speech-shaped noise and imitated dysphonic voice on first-grade primary school children’s speech perception and sentence comprehension. Via headphones, 5 to 6-year-old children, recruited within the French-speaking community of Belgium, listened to and performed a minimal-pair discrimination task and a sentence-picture matching task. Stimuli were randomly presented according to four experimental conditions: (1) normal voice / no noise, (2) normal voice / noise, (3) impaired voice / no noise, and (4) impaired voice / noise. The primary outcome measure was task score. How did performance vary with respect to listening condition? Preliminary results will be presented with respect to speech perception and sentence comprehension and carefully interpreted in the light of past findings. This study helps to support our understanding of children’s language processing skills under adverse conditions. Results shall serve as a starting point for probing new measures to optimize children’s learning environment.

Keywords: impaired voice, sentence comprehension, speech perception, speech-shaped noise, spoken language processing

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Authors: Ruphi Naz, Altaf Ahmad, Mohammad Anis

Abstract:

Sialic acid (5-Acetylneuraminic acid, Neu5Ac) is a common sugar found as a terminal residue on glycoconjugates in many animals. Humans brain and the central nervous system contain the highest concentration of sialic acid (as N-acetylneuraminic acid) where these acids play an important role in neural transmission and ganglioside structure in synaptogenesis. Due to its important biological function, sialic acid is attracting increasing attention. To understand metabolic networks, fluxes and regulation, it is essential to be able to determine the cellular and subcellular levels of metabolites. Genetically-encoded fluorescence resonance energy transfer (FRET) sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. Taking this, we developed a genetically encoded FRET (fluorescence resonance energy transfer) based nanosensor to analyse the sialic acid level in living cells. Sialic acid periplasmic binding protein (sia P) from Haemophilus influenzae was taken and ligated between the FRET pair, the cyan fluorescent protein (eCFP) and Venus. The chimeric sensor protein was expressed in E. coli BL21 (DE3) and purified by affinity chromatography. Conformational changes in the binding protein clearly confirmed the changes in FRET efficiency. So any change in the concentration of sialic acid is associated with the change in FRET ratio. This sensor is very specific to sialic acid and found stable with the different range of pH. This nanosensor successfully reported the intracellular level of sialic acid in bacterial cell. The data suggest that the nanosensors may be a versatile tool for studying the in vivo dynamics of sialic acid level non-invasively in living cells

Keywords: nanosensor, FRET, Haemophilus influenzae, metabolic networks

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Authors: Samsher Gautam, Apoorva Roy, Bhuvan Aggarwal

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Vapor absorption refrigeration systems can replace vapor compression systems in many applications as they can operate on a low-grade heat source and are environment-friendly. Widely used refrigerants such as CFCs and HFCs cause significant global warming. Natural refrigerants can be an alternative to them, among which carbon dioxide is promising for use in automotive air conditioning systems. Its inherent safety, ability to withstand high pressure and high heat transfer coefficient coupled with easy availability make it a likely choice for refrigerant. Various properties of the ionic liquid [bmim][PF₆], such as non-toxicity, stability over a wide temperature range and ability to dissolve gases like carbon dioxide, make it a suitable absorbent for a vapor absorption refrigeration system. In this paper, an absorption chiller consisting of a generator, condenser, evaporator and absorber was studied at an operating temperature of 70⁰C. A thermodynamic model was set up using the Peng-Robinson equations of state to predict the behavior of the refrigerant and absorbent pair at different points in the system. A MATLAB code was used to obtain the values of enthalpy and entropy at selected points in the system. The exergy destruction in each component and exergetic coefficient of performance (ECOP) of the system were calculated by performing an exergy analysis based on the second law of thermodynamics. Graphs were plotted between varying operating conditions and the ECOP obtained in each case. The effect of every component on the ECOP was examined. The exergetic coefficient of performance was found to be lesser than the coefficient of performance based on the first law of thermodynamics.

Keywords: [bmim][PF₆] as absorbent, carbon dioxide as refrigerant, exergy analysis, Peng-Robinson equations of state, vapor absorption refrigeration

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Authors: Shayli Varasteh Moradi, Wayne A. Johnston, Dejan Gagoski, Kirill Alexandrov

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The demand for a rapid and more efficient technique to identify protein-protein interaction particularly in the areas of therapeutics and diagnostics development is growing. The method described here is a rapid in vitro protein-protein interaction analysis approach based on AlphaLISA technology combined with Leishmania tarentolae cell-free protein production (LTE) system. Cell-free protein synthesis allows the rapid production of recombinant proteins in a multiplexed format. Among available in vitro expression systems, LTE offers several advantages over other eukaryotic cell-free systems. It is based on a fast growing fermentable organism that is inexpensive in cultivation and lysate production. High integrity of proteins produced in this system and the ability to co-express multiple proteins makes it a desirable method for screening protein interactions. Following the translation of protein pairs in LTE system, the physical interaction between proteins of interests is analysed by AlphaLISA assay. The assay is performed using unpurified in vitro translation reaction and therefore can be readily multiplexed. This approach can be used in various research applications such as epitope mapping, antigen-antibody analysis and protein interaction network mapping. The intra-viral protein interaction network of Zika virus was studied using the developed technique. The viral proteins were co-expressed pair-wise in LTE and all possible interactions among viral proteins were tested using AlphaLISA. The assay resulted to the identification of 54 intra-viral protein-protein interactions from which 19 binary interactions were found to be novel. The presented technique provides a powerful tool for rapid analysis of protein-protein interaction with high sensitivity and throughput.

Keywords: AlphaLISA technology, cell-free protein expression, epitope mapping, Leishmania tarentolae, protein-protein interaction

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Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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Authors: Farida Azzouz-Olden, Arthur G. Hunt, Gloria Degrandi-Hoffman

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Bees are confronting several environmental challenges, including the intermingled effects of malnutrition and disease. Intuitively, pollen is the healthiest nutritional choice; however, commercial substitutes, such as BeePro and MegaBee, are widely used. Herein we examined how feeding natural and artificial diets shapes transcription in the abdomen of the honey bee, and how transcription shifts in combination with Nosema parasitism. Gene ontology enrichment revealed that, compared with poor diet (carbohydrates (C)), bees fed pollen (P > C), BeePro (B > C), and MegaBee (M > C) showed a broad upregulation of metabolic processes, especially lipids; however, pollen feeding promoted more functions and superior proteolysis. The superiority of the pollen diet was also evident through the remarkable overexpression of vitellogenin in bees fed pollen instead of MegaBee or BeePro. Upregulation of bioprocesses under carbohydrates feeding compared to pollen (C > P) provided a clear poor nutritional status, uncovering stark expression changes that were slight or absent relatively to BeePro (C > B) or MegaBee (C > M). Poor diet feeding (C > P) induced starvation response genes and hippo signaling pathway, while it repressed growth through different mechanisms. Carbohydrate feeding (C > P) also elicited ‘adult behavior’, and developmental processes suggesting transition to foraging. Finally, it altered the ‘circadian rhythm’, reflecting the role of this mechanism in the adaptation to nutritional stress in mammals. Nosema-infected bees fed pollen compared to carbohydrates (PN > CN) upheld certain bioprocesses of uninfected bees (P > C). Poor nutritional status was more apparent against pollen (CN > PN) than BeePro (CN > BN) or MegaBee (CN > MN). Nosema accentuated the effects of malnutrition since more starvation-response genes and stress response mechanisms were upregulated in CN > PN compared to C > P. The bioprocess ‘Macromolecular complex assembly’ was also enriched in CN > PN, and involved genes associated with human HIV and/or influenza, thus providing potential candidates for bee-Nosema interactions. Finally, the enzyme Duox emerged as essential for guts defense in bees, similarly to Drosophila. These results provide evidence of the superior nutritional status of bees fed pollen instead of artificial substitutes in terms of overall health, even in the presence of a pathogen.

Keywords: honeybee, immunity, Nosema, nutrition, RNA-seq

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Authors: Patricia M. Boechler, Heather M. Gautreau

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In this research study, postsecondary students completed an information learning task in an avatar-based 3D virtual learning environment. Three factors were of interest in relation to learning; 1) the influence of collaborative vs. independent conditions, 2) the influence of the spatial arrangement of the virtual environment (linear, random and clustered), and 3) the relationship of individual differences such as spatial skill, general computer experience and video game experience to learning. Students completed pretest measures of prior computer experience and prior spatial skill. Following the premeasure administration, students were given instruction to move through the virtual environment and study all the material within 10 information stations. In the collaborative condition, students proceeded in randomly assigned pairs, while in the independent condition they proceeded alone. After this learning phase, all students individually completed a multiple choice test to determine information retention. The overall results indicated that students in pairs did not perform any better or worse than independent students. As far as individual differences, only spatial ability predicted the performance of students. General computer experience and video game experience did not. Taking a closer look at the pairs and spatial ability, comparisons were made on pairs high/matched spatial ability, pairs low/matched spatial ability and pairs that were mismatched on spatial ability. The results showed that both high/matched pairs and mismatched pairs outperformed low/matched pairs. That is, if a pair had even one individual with strong spatial ability they would perform better than pairs with only low spatial ability individuals. This suggests that, in virtual environments, the specific individuals that are paired together are important for performance outcomes. The paper also includes a discussion of trends within the data that have implications for virtual environment education.

Keywords: avatar-based, virtual environment, paired learning, individual differences

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Authors: Shveta Bathla, Preeti Rawat, Sudarshan Kumar, Rubina Baithalu, Jogender Singh Rana, Tushar Kumar Mohanty, Ashok Kumar Mohanty

Abstract:

Pregnancy is a complex process which includes series of events such as fertilization, formation of blastocyst, implantation of embryo, placental formation and development of fetus. The success of these events depends on various interactions which are synchronized by endocrine interaction between a receptive dam and competent embryo. These interactions lead to change in expression of hormones and proteins. But till date no protein biomarker is available which can be used to detect successful completion of these events. We employed quantitative proteomics approach to develop putative serological biomarker which has diagnostic applicability for early detection of pregnancy in cows. For this study, sera were collected from control (non-pregnant, n=6) and pregnant animals on successive days of pregnancy (7, 19, 45, n=6). The sera were subjected to depletion for removal of albumin using Norgen depletion kit. The tryptic peptides were labeled with iTRAQ. The peptides were pooled and fractionated using bRPLC over 80 min gradient. Then 12 fractions were injected to nLC for identification and quantitation in DDA mode using ESI. Identification using Mascot search revealed 2056 proteins out of which 352 proteins were differentially expressed. Twenty proteins were upregulated and twelve proteins were down-regulated with fold change > 1.5 and < 0.6 respectively (p < 0.05). The gene ontology studies of DEPs using Panther software revealed that majority of proteins are actively involved in catalytic activities, binding and enzyme regulatory activities. The DEP'S such as NF2, MAPK, GRIPI, UGT1A1, PARP, CD68 were further subjected to pathway analysis using KEGG and Cytoscape plugin Cluego that showed involvement of proteins in successful implantation, maintenance of pluripotency, regulation of luteal function, differentiation of endometrial macrophages, protection from oxidative stress and developmental pathways such as Hippo. Further efforts are continuing for targeted proteomics, western blot to validate potential biomarkers and development of diagnostic kit for early pregnancy diagnosis in cows.

Keywords: bRPLC, Cluego, ESI, iTRAQ, KEGG, Panther

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Authors: Concetta Busacca, Leone Frusteri, Orazio Di Blasi, Alessandra Di Blasi

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The large-scale extension of renewable energy led, recently, to the development of efficient and low-cost electrochemical energy storage (EES) systems such as batteries. Although lithium-ion battery (LIB) technology is relatively mature, several issues regarding safety, cyclability, and high costs must be overcome. Thanks to the availability and low cost of sodium, sodium-ion batteries (NIB) have the potential to meet the energy storage needs of the large-scale grid, becoming a valid alternative to LIB in some energy sectors, such as the stationary one. However, important challenges such as low specific energy and short cyclic life due to the large radius of Na+ must be faced to introduce this technology into the market. As an important component of SIBs, cathode materials have a significant effect on the electrochemical performance of SIBs. Recently, sodium layer transition metal oxides, phosphates, and organic compounds have been investigated as cathode materials for SIBs. In particular, phosphate-based compounds such as NaₓMPO₄ (M= Fe, Co, Mn) have been extensively studied as cathodic polyanion materials due to their long cycle stability and appropriate operating voltage. Among these, an interesting cathode material is the NaMnPO₄ based one, thanks to the stability and the high redox potential of the Mn²⁺/Mn³⁺ ion pair (3÷4 V vs. Na+/Na), which allows reaching a high energy density. This work concerns with the synthesis of a composite material based on NaMnPO₄ and carbon nanofibers (NaMnPO₄-CNF) characterized by a mixed crystalline structure between the maricite and olivine phases and a self-standing manufacture obtained by electrospinning technique. The material was tested in a Na-ion battery coin cell in half cell configuration, and showed outstanding electrocatalytic performances with a specific discharge capacity of 125 mAhg⁻¹ and 101 mAhg⁻¹ at 0.3C and 0.6C, respectively, and a retention capacity of about 80% a 0.6C after 100 cycles.

Keywords: electrospinning, self standing materials, Na ion battery, cathode materials

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Authors: Zhen Cheng, Xinyu Dai, Shujian Huang, Jiajun Chen

Abstract:

Recently, explanatory natural language inference has attracted much attention for the interpretability of logic relationship prediction, which is also known as explanation generation for Natural Language Inference (NLI). Existing explanation generators based on discriminative Encoder-Decoder architecture have achieved noticeable results. However, we find that these discriminative generators usually generate explanations with correct evidence but incorrect logic semantic. It is due to that logic information is implicitly encoded in the premise-hypothesis pairs and difficult to model. Actually, logic information identically exists between premise-hypothesis pair and explanation. And it is easy to extract logic information that is explicitly contained in the target explanation. Hence we assume that there exists a latent space of logic information while generating explanations. Specifically, we propose a generative model called Variational Explanation Generator (VariationalEG) with a latent variable to model this space. Training with the guide of explicit logic information in target explanations, latent variable in VariationalEG could capture the implicit logic information in premise-hypothesis pairs effectively. Additionally, to tackle the problem of posterior collapse while training VariaztionalEG, we propose a simple yet effective approach called Logic Supervision on the latent variable to force it to encode logic information. Experiments on explanation generation benchmark—explanation-Stanford Natural Language Inference (e-SNLI) demonstrate that the proposed VariationalEG achieves significant improvement compared to previous studies and yields a state-of-the-art result. Furthermore, we perform the analysis of generated explanations to demonstrate the effect of the latent variable.

Keywords: natural language inference, explanation generation, variational auto-encoder, generative model

Procedia PDF Downloads 125