COVID-19: Diagnosis, Management and Response

Background: COVID infection 2019 (COVID-19) is identified as a disease caused by Corona virus formally known as severe acute respiratory syndrome (SARS-CoV-2), which was first detected in Wuhan City, Hubei Province, China, amidst of an outbreak of respiratory sickness cases. Summary: As there is no affirmed total course of treatment for the Novel Coronavirus, the best way to handle it is by playing it safe, its administration and early reaction. Segregation and disinfection go inseparably with regards to dealing with a COVID positive patient. In any case, the predetermined number of clinical office accessible is blocking the cycle of control and anticipation for a particularly number of infected patients. Conclusion: As the pandemic is advancing, more examinations and exploration is needed to effectively deal with the spread of the novel Corona virus. Foundation improvement and arrangement of clinical office and gear is the preeminent prerequisite for early reaction and treatment.

A Compute and Wait in PoW (CW-PoW) Consensus Algorithm for Preserving Energy Consumption

Several trusted tasks use consensus algorithms to solve agreement challenges. Usually, consensus agreements are used to ensure data integrity and reliability in untrusted environments. In many distributed networking fields, the Proof of Work (PoW) consensus algorithm is commonly used. However, the standard PoW mechanism has two main limitations, where the first is the high power consumption and the second is the 51% attack vulnerability. In this paper, we look to improve the PoW consensus protocol by introducing several proof rounds. Any given consensus node should resolve the game of the current round Roundi before participating in the next round Roundi+1. Any node that resolves the game of Roundi can only pass to the next round if a predetermined number of solutions has been found by other nodes. The obtained evaluation results of this technique show significant improvements in terms of energy consumption and robustness against the 51% and Sybil attacks. By fixing the number of processes, we obtained an energy gain rate of 15.63% with five rounds and a gain rate of 19.91% with ten rounds.

Effect of vaccination on the apparent prevalence of bovine brucellosis in the state of Tocantins, Brazil

A cross-sectional study on the epidemiological situation of bovine brucellosis was carried out in the state of Tocantins to evaluate the effectiveness of its vaccination program. The state was divided into five regions, and a predetermined number of farms was randomly selected in each one. Females aged 24 months or older were randomly selected in each farm and diagnosed with brucellosis by serial serology (AAT and 2-ME). A total of 6,846 animals from 756 farms were examined. The prevalence of seropositive herds in the state was 6.42% [CI95%: 4.76-8.62], and the prevalence of seropositive animals was 2.21% [CI95%: 1.05-4.01]. The prevalence of seropositive herds was homogeneously distributed among regions. The 2002/2003 study estimated the prevalence of seropositive herds in the state to be 21.22% [CI95%: 19.33-23.11]. In conclusion, the vaccination program implemented in Tocantins, reaching vaccination coverage above 70% as of 2010, significantly reduced the prevalence of seropositive herds. Thus, continuing the vaccination program in the state is recommended, preferably increasing the quality of the processes involved, from commercialization to inoculation in animals, since immunization remains the most effective means to reduce the prevalence of brucellosis. In addition, animal replacement remains a major risk factor for bovine brucellosis in Tocantins since 20022003; therefore, the state must implement a strong health education program explaining to farmers the importance of testing animals for brucellosis before introducing them into their herds.

Epidemiological situation of bovine tuberculosis in the state of Tocantins, Brazil

The epidemiological situation of bovine tuberculosis was studied from October 2014 to August 2015 in the state of Tocantins, a traditional beef exporter. The state was divided into five regions and the farms were randomly selected in each region. A predetermined number of animals was randomly selected and diagnosed using the Comparative Cervical Tuberculin Test. A total of 11,926 animals from 757 farms were tested. Animals with inconclusive test results were retested with the same diagnostic procedure at least 60 days later. The prevalence of infected herds in the state was 0.16% [0.02-1.15] and the prevalence of infected animals 0.009% [0.001-0.063]. Only one positive animal was detected in the Araguaína region. The state of Tocantins has a very low prevalence of bovine tuberculosis and, therefore, should evaluate the need of structuring a surveillance system to eradicate the disease.

Organizational Prevention and Intervention Services

Improving the opportunities for enhanced health and wellness in first responders has gained national attention in recent years. Employers and other stakeholders striving to improve employee utilization of available resources will need to increase transparency in the process and improve understanding between first responders and clinicians. One potential process, early warning systems (EWS) are primarily designed to alert management to an at-risk employee. However, the continuing goal of any effort should be to identify and remedy any employee issue before the employee exhibits the predetermined number of events that trigger an early warning alert. Although many organizations are adopting an EWS by either choice or mandate, they are largely separate and distinct from the agency's health and wellness programming. Administrators are not only unsure of what data to consider, but also what to do when an alert is activated. Ideally, agencies move toward early intervention systems that themselves are conceptualized within the larger framework of wellness programming.

A Novel, Efficient, Unit Circle-Based, Method for Positioning and Operating Cryo-Surgical Probes in Convex Target Areas

A novel method for positioning and operating needle-like cryo-surgical probes in 2D convex target areas is presented. The method is based on the recorded dynamic performance of a single probe, termed “unit circle,” (UC) embedded in a semi-infinite, tissue-like medium. Up to 15 cryo-probes, inserted into the same depth, are operated uniformly for 2–5 min. A predetermined number of probes are rearranged inside the target area until a “tight configuration” is obtained. The probes are initially arranged inside the target area such that the “lethal temperature” circles produced by them are tangent to its contour and to both adjacent lethal temperature circles. Subsequently, all probes are repositioned inwardly, each at a specific distance that depends on the local radius of curvature of the target area. Resulting total “defect areas”—internal and external—for a number of demonstrated cases, amounted to between 2.5% and 7.6% of the target area. The lower values of the defect areas were obtained with increasing numbers of inserted probes coupled with shorter operating times. Possible freezing damages to regions beyond the target area were reduced by up to about 30% for these cases. Similar results were obtained for a case of combined convex-concave target area, treated with additional, externally inserted, heating probes.

Perfect representation of a convex 2D contour with only one Fourier component

Shape analysis of a closed 2D contour is an important topic within biological shape analysis, where Fourier methods to reproduce the shape with a limited number of parameters have been and still are of vital importance. An example is within marine management research on fish, where shape analysis of otolith (earstone) contours is performed for species identification as well as for stock discrimination purposes. In both cases, it is expected that the fewer parameters that are needed in a method to reproduce the contour sufficiently good, the better. This contribution outlines how a convex contour of any shape can be represented to any wanted accuracy by only one Fourier component. The key idea is to allow a flexible choice of a predetermined number of x-values along an x-axis that goes through the two most distant points of the contour. The y-variable along the perpendicular y-axis is then monotonically transformed to a z-variable so that the minium and maximum z-values on the contour have the same distance from the x-axis. The x-values of the contour points are now chosen so that the corresponding z-values on the contour follows a perfect sinusoid if the x-values were equidistant. The method is illustrated by application to lasso contours of Norwegian Coastal Cod (NCC) and North East Arctic Cod (NEAC) otolith images, where the average new x-positions for the individual otolith contours were applied to all otoliths. The results show that a considerably better fit to the original individual otolith contours were obtained by applying the invers FFT to the new y-values than by the frequently applied 2D EFDs (Elliptical Fourier Descriptors) approach, for the same number, m < 11, of frequency components. A promising classification result was also obtained by the linear Fisher discrimination method and cross validation applied to the individual x-values for the NCC and NEAC otoliths, with 82% score for NCC and 80% score for NEAC with sample sizes 367 and 240, respectively.

A Fast Algorithm to Initialize Cluster Centroids in Fuzzy Clustering Applications

The goal of partitioning clustering analysis is to divide a dataset into a predetermined number of homogeneous clusters. The quality of final clusters from a prototype-based partitioning algorithm is highly affected by the initially chosen centroids. In this paper, we propose the InoFrep, a novel data-dependent initialization algorithm for improving computational efficiency and robustness in prototype-based hard and fuzzy clustering. The InoFrep is a single-pass algorithm using the frequency polygon data of the feature with the highest peaks count in a dataset. By using the Fuzzy C-means (FCM) clustering algorithm, we empirically compare the performance of the InoFrep on one synthetic and six real datasets to those of two common initialization methods: Random sampling of data points and K-means++. Our results show that the InoFrep algorithm significantly reduces the number of iterations and the computing time required by the FCM algorithm. Additionally, it can be applied to multidimensional large datasets because of its shorter initialization time and independence from dimensionality due to working with only one feature with the highest number of peaks.

MODULAR SEARCH SPACE FOR AUTOMATED DESIGN OF NEURAL ARCHITECTURE

The past years of research have shown that automated machine learning and neural architecture search are an inevitable future for image recognition tasks. In addition, a crucial aspect of any automated search is the predefined search space. As many studies have demonstrated, the modularization technique may simplify the underlying search space by fostering successful blocks’ reuse. In this regard, the presented research aims to investigate the use of modularization in automated machine learning. In this paper, we propose and examine a modularized space based on the substantial limitation to seeded building blocks for neural architecture search. To make a search space viable, we presented all modules of the space as multisectoral networks. Therefore, each architecture within the search space could be unequivocally described by a vector. In our case, a module was a predetermined number of parameterized layers with information about their relationships. We applied the proposed modular search space to a genetic algorithm and evaluated it on the CIFAR-10 and CIFAR-100 datasets based on modules from the NAS-Bench-201 benchmark. To address the complexity of the search space, we randomly sampled twenty-five modules and included them in the database. Overall, our approach retrieved competitive architectures in averaged 8 GPU hours. The final model achieved the validation accuracy of 89.1% and 73.2% on the CIFAR-10 and CIFAR- 100 datasets, respectively. The learning process required slightly fewer GPU hours compared to other approaches, and the resulting network contained fewer parameters to signal lightness of the model. Such an outcome may indicate the considerable potential of sophisticated ranking approaches. The conducted experiments also revealed that a straightforward and transparent search space could address the challenging task of neural architecture search. Further research should be undertaken to explore how the predefined knowledge base of modules could benefit modular search space.

A modified crow search algorithm for the weapon-target assignment problem

The Weapon-Target Assignment (WTA) problem is one of the most important optimization problems in military operation research. In the WTA problem, assets of defense aim the best assignment of each weapon to target for decreasing expected damage directed by the offense. In this paper, Modified Crow Search Algorithm (MCSA) is proposed to solve the WTA problem. In MCSA, a trial mechanism is used to improve the quality of solutions using parameter LIMIT. If the solution is not improved after a predetermined number of iterations, then MCSA starts with a new position in the search space. Experimental results on the different sizes of the WTA problem instances show that MCSA outperforms CSA in all problem instances. Also, MCSA achieved better results for 11 out of 12 problem instances compared with four state-of-the-art algorithms. The source codes of MCSA for the WTA are publicly available at http://www.3mrullah.com/MCSA.html.