Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

This paper establishes a mathematical model for the resource management and scheduling of the fog node cluster and establishes the optimization goals of delay, communication load, and service cost. According to the idea of genetic algorithm for single-objective optimization, this paper proposes a linear weighted genetic algorithm based on linear weighting. The optimization weight is established according to the user’s preference for the target. We normalize the optimization objective function and merge it into one target, and then we proceed with genetic manipulation to get a better solution. The experimental results show that when the user specifies the preference weight, the optimal solution can be obtained by the genetic algorithm based on linear weighting, and the algorithm execution efficiency is high. With the increase of the single-objective weight, the optimization effect of this objective is better. When the preference weight tends to be average, its overall optimization effect is not ideal. When the user does not specify the preference weight, a set of optimal solutions can be obtained through the improved nondominated sorting genetic algorithm with elite strategy. Compared with the traditional algorithm, in addition to the overall optimization effect of the target being better, the algorithm itself also has higher efficiency.

Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials

IntroductionEngaging people with cystic fibrosis (CF) in clinical trials is critical to improving outcomes for this fatal disease. Following extensive exploration of engagement in CF trials we believe six key concepts require a quantitative understanding of their influence in the current CF trials landscape including how controversial issues like placebos, washouts, stipend provision and location of trial visits are viewed by the CF community and how these might be modified depending on the type of medicine being investigated and the mechanism of access to the drug on trial completion.Methods and analysisWe have designed and will administer an online discrete choice experiment to elicit and quantify preferences of people with CF for these trials’ attributes and estimate the relative importance of an attribute when choosing to participate in a trial. The cross-sectional data generated will be explored using conditional multinomial logit model. Mixed logit models such as the random-parameters logit and a latent class models will be used to explore preference heterogeneity. To determine the relative importance of an attribute, the difference between the attribute level with the highest preference weight and the level with the lowest preference weight will be calculated.Ethics and disseminationImperial College London Joint Research Compliance Office has granted ethical approval for this study. Patient consent will be sought following full explanation. No identifying information will be collected. Dissemination will be via international conferences, peer-review publication and patient accessible forums. Major CF trials networks have agreed to incorporate our findings into their review process, meaning our results can realistically influence and optimise CF trial delivery.PROSPERO registration numberCRD42020184886.

Product Module Attribute Parameter Configuration Model considering Customer Requirements Preferences

In the process of modular product configuration, it is necessary to transform customer requirements into product module attributes (PMA) parameters. However, previous research lacks consideration about customer requirement preference in the process of this transformation. First, we use a preference graph (PG) to obtain the customer preference weight vector for the requirement node. Second, on the basis of traditional Quality Function Deployment (QFD), the method of fuzzy correlation evaluation is introduced to get the correlation value between module attributes, and the combination programming model of PMA is further obtained by synthesizing the preference weight vector. Finally, the final configuration scheme is obtained by solving the model with the genetic algorithm. By integrating the weights of the above-mentioned nodes, the similarity of the product case is obtained, and a more satisfied case of the customer is obtained. Taking the automated guided vehicle car product as an example, the effectiveness and practicability of the proposed method are verified.

TINGKAT KESUKAN JENIS MAKANAN DAN PENGARUHNYA TERHADAP PERTAMBAHAN BERAT BADAN TIKUS EKOR PUTIH (Rattus xanthurus) DI PENANGKARAN EX-SITU

THE PREFERENCE OF FOOD TYPE AND THE INFLUENCE ON WEIGHT GAIN OF WHITE TAILED RAT (Rattus xanthurus) IN EX-SITU BREEDING. White tailed rats (Rattus xanthurus) is one of the endemic species of North Sulawesi that has the status of near threatened. The method used in this study is the method of observation. This research was conducted to provide information about the most preferred type of food and its effect on body weight gain, food consumption and the efficient use of white rat (Rattus xanthurus) so that they can be utilized in animal breeding business. The results showed that the type of food most preferred by the White Tailed Rat (Rattus xanthurus) female was papaya fruit 55.62 gr / head / day while males preferred the banana 49.28 gr / head / day. Weight gain in males is higher 17.79 g / head / day compared to females 10.09 g / head / day lower with 60 gr / head / day feeding. The highest efficiency of food use is found in males, this condition can be interpreted that the amount of consumption is enough to contribute to the weight gain of white tailed rats (Rattus xanthurus). The results of this study it can be concluded that the white tailed rat (Rattus xanthurus) prefers papaya fruit compared to other fresh foods.Keywords: Preference, Weight gain, White-tailed rat

Decision Support System for Determination of Salaries Employees in Civil Service Police Unit Deli Serdang With Simple Additive weighting method (SAW)

This study examines how to design an application for the Deli Serdang Civil Service Police Unit in order to Facilitate the Process of Determining Salaries employee. With this application, it is hoped that it will make it Easier for the Office to Determine the determination of employee Salaries so that it is faster and more accurate. The SAW method certainly uses a more Appropriate assessment Because it is based on a criterion value of a predetermined preference weight. With the existence of accurate the data will be Able to minimize the time wasted and Be Able to use the system that is being designed. Data managed in the Office still uses Excel so that in its implementation the the data is still vulnerable to being scattered and the possibility of vulnerability to damage and loss of data.

How Do Older Adults Consider Age, Life Expectancy, Quality of Life, and Physician Recommendations When Making Cancer Screening Decisions? Results from a National Survey Using a Discrete Choice Experiment

Background. Older adults with limited life expectancy frequently receive cancer screening, although on average, harms outweigh benefits. We examined the influence of life expectancy on older adults’ cancer screening decisions relative to three other factors. Methods. Adults aged 65+ years ( N = 1272) were recruited from a national online survey panel. Using a discrete choice experiment, we systematically varied a hypothetical patient’s life expectancy, age, quality of life, and physician’s recommendation and asked whether the participant would choose screening. Participants were randomized to questions about colonoscopy or prostate-specific antigen/mammography screenings. Logistic regression produced preference weights that quantified the relative influence of the 4 factors on screening decisions. Results. 879 older adults completed the survey, 660 of whom varied their screening choices in response to the 4 factors we tested. The age of the hypothetical patient had the largest influence on choosing screening: the effect of age being 65 versus 85 years had a preference weight of 2.44 (95% confidence interval [CI]: 2.22, 2.65). Life expectancy (10 versus 1 year) had the second largest influence (preference weight: 1.64, CI: 1.41, 1.87). Physician recommendation (screen versus do not screen) and quality of life (good versus poor) were less influential, with preference weights of 0.90 (CI: 0.72, 1.08) and 0.68 (CI: 0.52, 0.83), respectively. Conclusions. While clinical practice guidelines increasingly use life expectancy in addition to age to guide screening decisions, we find that age is the most influential factor, independent of life expectancy, quality of life, and physician recommendation, in older adults’ cancer screening choices. Strategies to reduce overscreening should consider the importance patients give to continuing screening at younger ages, even when life expectancy is limited.