An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.

Videogrammetric Verification of Accuracy of Wearable Sensors Used in Kiteboarding

Owing to the combination of windsurfing, snowboarding, wakeboarding, and paragliding, kiteboarding has gained an enormous number of fans worldwide. Enthusiasts compete to achieve the maximum height and length of jumps, speed, or total distance travelled. Several commercially available systems have been developed to measure these parameters. However, practice shows that the accuracy of the implemented sensors is debatable. In this study, we examined the accuracy of jump heights determined by sensors WOO2 and WOO3, and the Surfr app installed on an Apple iPhone SE 2016, compared to a combination of videogrammetric and geodetic measurements. These measurements were performed using four cameras located on the shore of the Danube River at Šamorín, Slovakia. The videogrammetrically-determined accuracy of jump heights was 0.03–0.09 m. This can be considered a reference for comparing the accuracy of off-the-shelf systems. The results show that all of the systems compared tend to overestimate jump heights, including an increase in error with increasing jump height. For jumps over 5 m, the deviations reached more than 20% of the actual jump height.

Bioactive cationic peptides as potential agents for breast cancer treatment

Breast cancer continues to affect millions of women worldwide, and the number of new cases dramatically increases every year. The physiological causes behind the disease are still not fully understood. One in every 100 cases can occur in men, and although the frequency is lower than among women, men tend to have a worse prognosis of the disease. Various therapeutic alternatives to combat the disease are available. These depend on the type and progress of the disease, and include chemotherapy, radiotherapy, surgery, and cancer immunotherapy. However, there are several well-reported side effects of these treatments that have a significant impact on life quality, and patients either relapse or are refractory to treatment. This makes it necessary to develop new therapeutic strategies. One promising initiative are bioactive peptides, which have emerged in recent years as a family of compounds with an enormous number of clinical applications due to their broad spectrum of activity. They are widely distributed in several organisms as part of their immune system. The antitumoral activity of these peptides lies in a nonspecific mechanism of action associated with their interaction with cancer cell membranes, inducing, through several routes, bilayer destabilization and cell death. This review provides an overview of the literature on the evaluation of cationic peptides as potential agents against breast cancer under different study phases. First, physicochemical characteristics such as the primary structure and charge are presented. Secondly, information about dosage, the experimental model used, and the mechanism of action proposed for the peptides are discussed.

Multiple Applications of Different Exercise Modalities with Rodents

A large proportion of chronic diseases can be derived from a sedentary lifestyle. Raising physical activity awareness is indispensable, as lack of exercise is the fourth most common cause of death worldwide. Animal models in different research fields serve as important tools in the study of acute or chronic noncommunicable disorders. With the help of animal-based exercise research, exercise-mediated complex antioxidant and inflammatory pathways can be explored, which knowledge can be transferred to human studies. Whereas sustained physical activity has an enormous number of beneficial effects on many organ systems, these animal models are easily applicable in several research areas. This review is aimed at providing an overall picture of scientific research studies using animal models with a focus on different training modalities. Without wishing to be exhaustive, the most commonly used forms of exercise are presented.

Recent Developments and Aspects of Industrial Fluoroalkylation

Fluoroalkylations have received increasing attention in the academic and industrial environment due to the particular properties of the active ingredients that are strongly influenced by fluoroalkyl substituents. The inherent difficulties of introducing a fluoroalkyl substituent into advanced intermediates has triggered the development of an enormous number of specialized reagents, which, however, are often not suitable for large scale applications. In contrast to this reagent based fluoroalkylation approach, the direct activation of industrially readily available fluoroalkyl halides could be more suitable for a large-scale process. In this way the dithionite initiated fluoroalkylation as well as newly developed catalytically activated fluoroalkylation protocols were considered for industrial large-scale applications.

A Framework for the Study of Persuasion

Persuasion is a vital part of politics—who wins elections and policy disputes often depends on which side can persuade more people. Given this centrality, the study of persuasion has a long history with an enormous number of theories and empirical inquiries. However, the literature is fragmented, with few generalizable findings. I unify previously disparate dimensions of this topic by presenting a framework focusing on actors (speakers and receivers), treatments (topics, content, media), outcomes (attitudes, behaviors, emotions, identities), and settings (competition, space, time, process, culture). This Generalizing Persuasion (GP) Framework organizes distinct findings and offers researchers a structure in which to situate their work. I conclude with a discussion of the normative implications of persuasion. Expected final online publication date for the Annual Review of Political Science, Volume 25 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Bioactive Cationic Peptides as Potential Agents for Breast Cancer Treatment

Breast cancer continues to affect millions of women worldwide, and the number of new cases dramatically increases every year. The physiological causes behind the disease are still not fully understood. One in every 100 cases can occur in men, and although the frequency is lower than among women, men tend to have a worse prognosis of the disease. Various therapeutic alternatives to combat the disease are available. These depend on the type and progress of the disease, and include chemotherapy, radiotherapy, surgery, and cancer immunotherapy. However, there are several well-reported side effects of these treatments that have a significant impact on life quality, and patients either relapse or are refractory to treatment. This makes it necessary to develop new therapeutic strategies. One promising initiative are bioactive peptides, which have emerged in recent years as a family of compounds with an enormous number of clinical applications due to their broad spectrum of activity. They are widely distributed in several organisms as part of their immune system. The antitumoral activity of these peptides lies in a nonspecific mechanism of action associated with their interaction with cancer cell membranes, inducing, through several routes, bilayer destabilization and cell death. This review provides an overview of the literature on the evaluation of cationic peptides as potential agents against breast cancer under different study phases. First, physicochemical characteristics such as the primary structure and charge are presented. Secondly, information about dosage, the experimental model used, and the mechanism of action proposed for the peptides are discussed.

Covid-on-the-Web: Exploring the COVID-19 Scientific Literature through Visualization of Linked Data from Entity and Argument Mining

The unprecedented mobilization of scientists, consequent of the COVID-19 pandemics, has generated an enormous number of scholarly articles that is impossible for a human being to keep track and explore without appropriate tool support. In this context, we created the Covid-on-the-Web project, which aims to assist the access, querying, and sense making of COVID-19 related literature by combining efforts from semantic web, natural language processing, and visualization fields. Particularly, in this paper, we present (i) an RDF dataset, a linked version of the “COVID-19 Open Research Dataset” (CORD-19), enriched via entity linking and argument mining, and (ii) the “Linked Data Visualizer” (LDViz), 28 which assists the querying and visual exploration of the referred dataset. The LDViz tool assists the exploration of different views of the data by combining a querying management interface, which enables the definition of meaningful subsets of data through SPARQL queries, and a visualization interface based on a set of six visualization techniques integrated in a chained visualization concept, which also supports the tracking of provenance information. We demonstrate the potential of our approach to assist biomedical researchers in solving domain-related tasks, as well as to perform exploratory analyses through use case scenarios.

A Reliable Low Power Multiplier Using Fixed Width Scalable Approximation

Recent Approximate computing is a change in perspective in energy-effective frameworks plan and activity, in light of the possibility that we are upsetting PC frameworks effectiveness by requesting a lot of precision from them. Curiously, enormous number of utilization areas, like DSP, insights, and AI. Surmised figuring is appropriate for proficient information handling and mistake strong applications, for example, sign and picture preparing, PC vision, AI, information mining and so forth Inexact registering circuits are considered as a promising answer for lessen the force utilization in inserted information preparing. This paper proposes a FPGA execution for a rough multiplier dependent on specific partial part-based truncation multiplier circuits. The presentation of the proposed multiplier is assessed by contrasting the force utilization, the precision of calculation, and the time delay with those of a rough multiplier dependent on definite calculation introduced. The estimated configuration acquired energy effective mode with satisfactory precision. When contrasted with ordinary direct truncation proposed model fundamentally impacts the presentation. Thusly, this novel energy proficient adjusting based inexact multiplier design outflanked another cutthroat model.

Dark QED from inflation

One contribution to any dark sector’s abundance comes from its gravitational production during inflation. If the dark sector is weakly coupled to the inflaton and the Standard Model, this can be its only production mechanism. For non-interacting dark sectors, such as a free massive fermion or a free massive vector field, this mechanism has been studied extensively. In this paper we show, via the example of dark massive QED, that the presence of interactions can result in a vastly different mass for the dark matter (DM) particle, which may well coincide with the range probed by upcoming experiments.In the context of dark QED we study the evolution of the energy density in the dark sector after inflation. Inflation produces a cold vector condensate consisting of an enormous number of bosons, which via interesting processes — Schwinger pair production, strong field electromagnetic cascades, and plasma dynamics — transfers its energy to a small number of “dark electrons” and triggers thermalization of the dark sector. The resulting dark electron DM mass range is from 50 MeV to 30 TeV, far different from both the 10−5 eV mass of the massive photon dark matter in the absence of dark electrons, and from the 109 GeV dark electron mass in the absence of dark photons. This can significantly impact the search strategies for dark QED and, more generally, theories with a self-interacting DM sector. In the presence of kinetic mixing, a dark electron in this mass range can be searched for with upcoming direct detection experiments, such as SENSEI-100g and OSCURA.