Accurate Parameters Identification of a Supercapacitor Three-Branch Model

Supercapacitors are becoming increasingly important storage system components. To effectively control their terminal voltage, even in real time, numerous circuit models capable of faithfully simulating their behavior in energy systems and various applications are being explored. The three-branch supercapacitor model appears to be a good compromise between simplicity and accuracy. Typically, this model lacks accuracy in dynamic cycling and long stand-by periods. In this study, a new model identification method based on the state equations of the circuit is described and tested on a 400 F supercapacitor, and the obtained results are validated by measurements. Such an approach, suitably optimized, provides good agreement with the measurements, with discrepancies below 50 mV even in repeated cycles. In the static identification, after 90 minutes of self-discharge, the discrepancy was approximately 5 mV. The study also discusses the sensitivity of the model output to the circuit parameters, which is useful for choosing the appropriate timespan for parameter optimization and introduces variable leakage resistance and a method for its determination. Through this parameter, good agreement with the measurements is observed during the long self-discharging phases. A discrepancy of less than 50 mV between the measured and computed results is observed after one week. The union of the circuit state equations based model and the nonlinear leakage resistance determination allows the three-branch circuit model to achieve a high accuracy both in real-time simulation and in the presence of long stand-by phases

1st International Workshop on Tabular Data Analysis (TaDA)

With the advent of data lakes and open data repositories containing heterogeneous collections of structured datasets, there is an increasing need for automated methods to analyze tabular data collections for a wide range of applications in data management, data science, and decision support. Our goal in this workshop was to bring together researchers and practitioners working on building such tabular data analysis solutions. TaDa workshop aimed to provide a venue for the growing number of researchers in data management, AI, and Semantic Web communities working on a wide range of problems relevant to tabular data analysis. The first edition of the workshop included two keynote talks, a research track comprising presentations and posters, and invited posters and virtual talks of the work done in these communities

Results of SemTab 2020

SemTab 2020 was the second edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 19th International Semantic Web Conference (ISWC) and the 15th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of-the-art systems

Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness

Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events. In recognition of these concerns, the United States Federal Drug Administration (FDA) requires drug product labels to include specific information. Unfortunately, several studies have found that drug product labeling fails to keep current with the scientific literature. We present a novel approach to addressing this issue. The primary goal of this novel approach is to better meet the information needs of persons who consult the drug product label for information on a drug's efficacy, effectiveness, and safety. Using FDA product label regulations as a guide, the approach links drug claims present in drug information sources available on the Semantic Web with specific product label sections. Here we report on pilot work that establishes the baseline performance characteristics of a proof-of-concept system implementing the novel approach. Claims from three drug information sources were linked to the Clinical Studies, Drug Interactions, and Clinical Pharmacology sections of the labels for drug products that contain one of 29 psychotropic drugs. The resulting Linked Data set maps 409 efficacy/effectiveness study results, 784 drug-drug interactions, and 112 metabolic pathway assertions derived from three clinically-oriented drug information sources (ClinicalTrials.gov, the National Drug File - Reference Terminology, and the Drug Interaction Knowledge Base) to the sections of 1,102 product labels. Proof-of-concept web pages were created for all 1,102 drug product labels that demonstrate one possible approach to presenting information that dynamically enhances drug product labeling. We found that approximately one in five efficacy/effectiveness claims were relevant to the Clinical Studies section of a psychotropic drug product, with most relevant claims providing new information. We also identified several cases where all of the drug-drug interaction claims linked to the Drug Interactions section for a drug were potentially novel. The baseline performance characteristics of the proof-of-concept will enable further technical and user-centered research on robust methods for scaling the approach to the many thousands of product labels currently on the market

Results of SemTab 2022

SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of-the-art systems. In this paper, we give an overview of the 2022’s edition of the challenge and summarize the results

Multi-period Project Portfolio Selection under Risk considerations and Stochastic Income

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably

Results of SemTab 2021

SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of-the-art systems

Role of life events in the presence of colon polyps among African Americans

African Americans have disproportionately higher incidence and death rates of colorectal cancer among all ethnic groups in the United States. Several lifestyle factors (e.g. diet, physical activity and alcohol intake) have been suggested as risk factors for colorectal cancer. Stressful life events have also been identified as risk factors for colorectal cancer. The association between stressful life events and colon polyps, which are precursors of colorectal cancer, has yet to be determined. We aimed to evaluate the relationship between stressful life events and the presence of colon polyps and adenomas in African American men and women. In this cross-sectional study, 110 participants were recruited from a colon cancer screening program at Howard University Hospital. Participants completed an 82-item Life Events Questionnaire (Norbeck 1984), assessing major events that have occurred in the participants’ life within the past 12 months. Participants also reported whether the event had a positive or negative impact. Three scores were derived (total, positive, and negative). Total life events scores were higher (Median [M] = 29 and Interquartile range [IQR] = 18-43) in patients with one or more polyps compared to patients without polyps (M, IQR = 21,13-38; P = 0.029). Total, positive or negative Life Events scores did not differ significantly between normal and adenoma patients. Total, negative and positive Life Events scores did not differ between patients who underwent diagnostic colonoscopy (symptomatic) and patients who underwent colonoscopy for colon cancer screening (asymptomatic) and patients for surveillance colonoscopies due to a personal history of colon polyps. Linear regression analysis indicated that male gender is associated with 9.0 unit lower total Life Events score (P = 0.025). This study suggests that patients who experienced total life events may be at higher risk of having colon polyps and adenomas which indicates an association between stress and the development of colorectal polyps.https://doi.org/10.1186/1471-230X-13-10