Excellent practice. You have a say in which weught are turned on Organic stamina formulas off. Safe weight optimization Sare relationship optimizatoin them is shown in Fig. Weight reductions through structural optimization of products are important tasks for developers. In this part, the grouping strategy we study can be classified into ideal grouping and non-ideal grouping e.

Safe weight optimization -

We offer weight management plans that incorporate different treatments and techniques to help you lose weight so that you can look and feel your best. Then, once you reach a healthy weight, we can help you further optimize your body by fine-tuning different body areas with body contouring treatments, peptide therapy, and other treatments.

In designing the right weight management plan for you, we will consider many different factors, including the state of your metabolism, your diet, and your fitness routine.

We can prescribe medication, guide you in choosing the right detoxification diet, and provide you with the accountability you need to achieve your weight loss goals and maintain them long-term. Your metabolism is critical when it comes to health, wellness, and healthy body composition.

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Our goal is to help optimize your metabolism so that it can run smoothly and utilize the food you eat effectively. We offer different treatments that will help optimize your metabolism, increase the rate at which it works, and help it work more efficiently. In doing so, we will work to increase your energy levels so that you can burn those last few pounds of fat and reach your body goals quickly and effectively without spending extra hours at the gym.

Another important aspect of becoming the best version of yourself is knowing how and when to exercise. We will provide you with guidance about your fitness routine so that you know what to do and when to do it so that you can build lean muscle mass, tone and define your body, and promote cardiovascular health.

Working out, to some extent, is for show. It makes you look better and gives you a sense of confidence in your body, especially when you start to make gains at the gym, increase muscle mass, and lose fat. It promotes circulation, increases oxygen to the skin and organs, and is good for your physical, mental, and emotional health.

Without exercise, your body cannot be its best. Even if you only aim for 30 minutes every day of the week, you can ensure that as long as you are exercising, you are taking the steps you need to help your body and yourself become the best version it can be.

Hormonal imbalances often affect men during andropause and women during menopause, but they can also affect younger men and women. By performing the necessary diagnostics, we can determine if you are deficient in specific hormones and take steps to balance them so that you can look and feel your best.

They also have increased energy levels to perform their workouts and live a vibrant life. Peptide therapy is a natural yet effective form of anti-aging medicine that can help you achieve your goals. Peptide therapy can also help improve skin elasticity because tighter skin always leads to more defined-looking muscles and a more youthful appearance.

In choosing the right peptides for your goals, we will take different factors into account and your unique needs. We often add body contouring treatments to our body optimization plans to help patients define, sculpt, and transform their bodies by using a device called Contoura.

In the months that follow your treatment, the body slowly eliminates the destroyed fat cells, and you appear tighter and more toned. If you are looking for a way to lose weight, tighten and tone your body, and eliminate cellulite and other problem areas by taking your body to the next level, you are a good candidate for a body optimization plan.

We can help your body work more efficiently so that you can look and feel your best ever. The two-tuple linguistic representation is based on the concept of symbol transfer Qi et al. It adopts a two-tuple s i , α i to represent language evaluation information.

It indicates the deviation between the calculated evaluation result and the nearest phrase in S. For example, the triangular fuzzy value for the granularity of five is shown in Table 2. For the s i and s j , the size between them is determined by the following rules:.

The matrix is denoted as Z. Algorithm 1 provides the method to obtain the matrix Z. The accident influences in weight optimization of evaluation indicators are mainly affected by factors such as the occurrence time, the hazard degree, and the revised contribution degree.

The method to quantify these factors is presented in this section. The hazard degree of an accident is the quantitative analysis score of the influence of the accident damage.

Different industries or fields are involved in different factors of accident hazards. The indicators of each level are different.

Generally, the following four elements are used to describe the accident hazards in the chemical industry, which are personal injury, property loss, environmental influence, and social influence. For example, Table 3 is a common quantitative scoring table for the accidents in Sinopec, which gives different scores for different levels of the hazards.

The hazard degree of accident is the score obtained after the hazard evaluation for an accident according to Table 3. DEL is used to denote the direct economic loss. Each accident sample is assigned an evaluation score of hazard degree from the above four aspects.

The highest one is selected as the quantified value of the accident hazard. The hazard degree of accident for the i -th sample is symbolized as h i.

The correlation intensity matrix Z is obtained by linguistic weighting from the correlation evaluation. The correlation between an accident and evaluation indicators may be evaluated as a diversified importance due to the differences of knowledge and perspective of accident analysts.

The greater the difference between the correlation importance of an accident and each evaluation indicator, the higher the revised contribution degree of the accident to distinguish each evaluation indicator from all the others, and vice versa.

Therefore, the revised contribution degree is determined by the dispersion degree of correlation importance. In Section 3 , we have presented the definition of the bipartite graph of accident cases and evaluation indicators.

How to obtain the influence of the accident node and correlation intensity between the accident nodes and the evaluation indicators are also investigated. To use a random walk way to optimize the weights of evaluation indicators, we add weight values to the nodes and edges of our proposed bipartite graph.

The initial weight of evaluation indicator node is set as zero, i. We obtain a new weight value for the evaluation indicator node by a random walk method. PersonalRank, a random walk algorithm based on bipartite graph, is one of the frequently used algorithms to compute node correlations Hu et al.

It is often used in personalized recommendation of various commodities based on the network structure Li et al. Commodity recommendation to users can be understood as the redistribution of the commodity resources Wei et al.

The idea of commodity recommendation can be transferred to the optimization of indicator weight. The access probability of node v j in the PersonalRank algorithm is shown in Equation Here, r j represents the access probability of node v j , and λ is the probability of random walk.

The parameter p j is assigned as 1 if the node v j is an evaluation indicator node, otherwise it is set as 0. out k represents the number of all outgoing edges of node v k , and in v j represents the set of incoming edges pointing to node v j.

m 0 is the number of samples while T t 0 is the time weight of the original indicator system. Here, the algorithm 2 presents the proposed indicator weight optimization method based on the improved PersonalRank algorithm.

We present two ways to verify the effectiveness of the proposed method. The first one is a ranking evaluation method. It does not consider the accuracy of indicator weights. We only test whether the ranking distribution of the optimized indicator weights are consistent with the ranking of the indicator weights given by the experts.

The second one is an evaluation method of distribution fitness. It evaluates the accuracy of weight assignment by verifying which indicator system fits better with the acknowledged excellent indicator system.

The correlation similarity is greater if their correlation coefficient is with a high absolute value. The computing method of the correlation coefficients is shown in Equation 16 — The distribution fitness method is used to evaluate the assignment accuracy of indicator weights. K-L divergence is introduced to evaluate distribution fitness between two indicator systems.

K-L divergence is a method to describe the difference between two probability distributions. The evaluation indicators are regarded as random variables while indicator weights are regarded as different values of these random variables.

In this way, K-L divergence can be used to measure the fitting degree between two evaluation indicator systems.

According to the definition of K-L divergence, we know that two indicator systems can be evaluated by the distribution fitness if and only if their have the same indicator items. However, it is difficult to find two identical indicator systems in practice.

However, there are many overlapping indicators in the similar evaluation systems. For example, there are nearly related evaluation indicators between the international ISRS indicator system and the Chinese chemical industry indicator system. We assume that the weight distribution of the subset evaluation indicators is consistent with the original evaluation indicators.

In this way, the weights of the evaluation indicators optimized in this paper can be evaluated based on the distribution fitting performance. Two evaluation systems are correlated with each other if they have cognate mapping indicators. Correlation graph between indicator system I 1 and I 2.

Thus, we can get the conclusion that the projection indicator system extracted from the above is a subset of the original indicator system. The indicator weights were optimized by our proposed method. Ranking evaluation and distribution fitting evaluation are employed to verify the effectiveness of the proposed method.

The correlation evaluation described by different language granularity is transformed into quantitative correlation evaluation based on the two-tuple linguistic method. These value forms the weights of correlation matrix. According to the occurrence time, hazard degree, and revised contribution degree of accident samples, the initial weights of accident samples are calculated.

The random walk algorithm is used to optimize the indicator weights. Qualitative evaluation of indicator ranking. Evaluation of weight fitting degree.

We evaluate whether the fitting degree between the optimized indicator system and the international safety evaluation system is higher than that between the original indicator system and the international safety evaluation system by the K - L divergence.

We have developed an optimization system for indicator weights. The proposed methods have been integrated into this system. The following data are obtained in the developed system.

The indicator system is modified by referring to the several foreign indicator systems and combining them with the actual situation in China. The inclusion relationship between them is shown in Fig.

We have conducted a statistical analysis about the occurrence time of accident and the accident types of different enterprises in the analysis reports. Percentage figures of various categories are shown in Fig. The accident analysts of the safety research institute have given the relevance importance between the root cause of each accident case and the evaluation indicators.

In the evaluation of indicator importance, five types of language evaluation sets with different granularities are provided, and the evaluation granularities are 3, 5, 7, 9, and Different experts can use their own professional knowledge and experience to judge the importance of indicators by selecting evaluation sets with different granularities.

Then the correlation intensity was obtained by the two-tuple linguistic group decision-making method. Furthermore, accident analysts also presented the accident hazard score for each sample case according to Table 3.

According to the hierarchical ranking method, they only gave importance evaluation for the indicators at the same level. The indicators with the same serial number can appear at the same level. For example, there are five direct subindicators 8.

The experts only need to present their partial rankings. The ranking values of 10 experts were selected to form the basis of ranking evaluation measures in this paper. There are some well-known safety evaluation indicator systems in the world. Some evaluation indicator systems are built for multi-industry and their indicators are coarse-grained.

Some are internal evaluation systems for enterprises. Different evaluation systems are assigned with different evaluation indicators. They are different in the number of indicators and indicator weights. We selected three well-known chemical evaluation indicator systems for correlation analysis to form the basis of fitting degree evaluation.

S 1 is a well-known chemical company in Germany. The system is divided into four levels with evaluation indicators. S 2 is a well-known safety evaluation service organization in Norway. The ISRS safety rating system designed by it has been widely used in many industries.

Its current version contains 15 first-level indicators, second-level indicators, and three-level indicators. S 3 is a risk management company in South Africa, providing risk management solutions for safety, health, environment, and quality.

Its evaluation system includes 12 first-level indicators, 88 second-level indicators, and three-level indicators. Table 4 shows the numbers of these correlation indicators.

Consensus is a very important issue in the ranking evaluation method. These experts have reached consensus to collective opinion to guarantee the quality of the ranking. Figures 6 , 7 , and 8 show the ranking comparison of first-level, some second-level, and third-level indicators. We can see that the original indicator ranking, the expert indicator ranking, and our indicator ranking are very close.

The bending degree of the polyline ranked by our method is roughly the same as the expert ranking polyline. Meanwhile, Fig. In Fig. The red part represents the expert ranking while the blue one represents the original indicators ranking.

We can see that the fitness of red area and green area is better than that of red area and blue area. From the analysis of 8. Tables 5 , 6 , and 7 show the comparison results of these correlation coefficients for the different level indicators.

From the comparison of indicator ranking and the comparative analysis of correlation coefficients, we can find that the first-level indicators have the strongest correlation, the second-level indicators get the second place, and the third-level indicators are with the weakest correlation.

Compared with the original indicator ranking, the proposed method has improved the correlation coefficient of the first-level indicators up to 1. The indicator ranking optimized by our method is closer to the expert indicator ranking than the original indicator ranking.

We verify the proposed weight optimization method based on the well-known excellent indicator systems in this section.

Three excellent indicator systems S 1, S 2, and S 3 are employed to perform the distribution fitness analysis. According to Table 4 , the indicator numbers of the projection indicator system for S 1, S 2, and S 3 are , , and , respectively. Taking the weights of the excellent indicator systems as the reference objects, the calculation of the distribution fitting degrees of the optimized indicator system, the original indicator system, and the excellent indicator systems are converted into computing the D KL value of the projection indicator systems and the cognate mapping indicator systems.

Let I 1 and I 2 be the original indicator system and the optimized indicator system by the proposed method, respectively. Figures 10 , 11 , and 12 show the indicator weight fittings of the original indicator system, the indicator system optimized by our method with the excellent practice S 1, S 2, and S 3.

The weight fitting degrees between the optimized indicator system, the original indicator system, and the excellent practice are shown in Table 8. Compared with the original indicator system, the optimized indicator system has a higher distribution fitting with the three excellent practices.

By a horizontal comparison with the three excellent practices, S 2 has the most significant number of related indicators with the evaluation indicator system. The fitting degree is also the highest.

However, there is no apparent correlation between the fitting degree and the number of related indicators of the two evaluation indicator systems. In this study, we proposed a comprehensive weighting method based on the ABG. The proposed weighting method can consider both objective influences of the accidents and the subjective evaluations of experts.

A random walk algorithm was designed to realize the weight optimization. Meanwhile, the indicator weights optimized by our method shows a higher distribution fitting degree than the original indicator system. Thus, it can be concluded that the proposed indicator weight optimization method is effective and advanced.

In future research, the optimization effect of indicator weights will be demonstrated through safety evaluations of chemical enterprises. Moreover, we will further explore the factors involved in the accident influence that can play feedback on the weight optimization.

This work is supported by the Natural Science Foundation of China under grant , the Natural Science Foundation of Shandong Province under grant ZRMF and ZRMF, and the Key Research Program of Shandong Province Soft Sciences under grant RKY Altintas K.

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Sign In or Create an Account. Navbar Search Filter Journal of Computational Design and Engineering This issue Engineering and Technology Human-Computer Interaction Mathematical Theory of Computation Science and Mathematics Technical Design Books Journals Oxford Academic Mobile Enter search term Search.

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Article Contents Abstract. List of symbols. Related Work. Bipartite Graph of Accident Cases and Evaluation Indicators. Indicator Weight Optimization Method Based on Random Walk. Performance Evaluation for the Weight Optimization Method. Experiment and Case Analysis.

Conflict of Interest Statement. Journal Article. A weight optimization method for chemical safety evaluation indicators based on the bipartite graph and random walk. Junwei Du , Junwei Du. College of Information Science and Technology, Qingdao University of Science and Technology.

Oxford Academic. Guanghui Jing. Qiang Hu. Corresponding author. E-mail: huqiang Revision received:. Corrected and typeset:. PDF Split View Views. Select Format Select format. ris Mendeley, Papers, Zotero. enw EndNote. bibtex BibTex. txt Medlars, RefWorks Download citation.

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Graphical Abstract. Open in new tab Download slide. weight optimization , chemical safety , bipartite graph , random walk. The j- th evaluation indicator. The set of evaluation indicator nodes. Influence of accident case node v i. The k -th phrase in S. Distance between s j and s i.

Variance of s i. Gravity center of s i. Time weight of accident case sample. Accident hazard value of the i -th sample. Influence weight of accident node.

Revised contribution degree for the accident i. Access probability of node v j. Number of outgoing edges for node v k. Edge set pointing to node v j. Weight proportion of evaluation indicator l j.

The number of samples in original indicator system. The weight proportion of evaluation indicator l j. The weight of evaluation indicator. Target ranking weight of the evaluation indicators.

Ranking weight of the original indicator system. Safety evaluation indicator system. Weight fitting degree of I 1 and I 2. Projection of the indicator system. Table 1: The differences in the existing weighting methods. Method type. Method character. Objective method TOPSIS Optimize indicator weights by the principle of shortest distance from the ideal solution and greatest distance from the negative ideal solution.

It is a great challenge for optimizwtion evolutionary algorithms EAs Save tackle large-scale B vitamins for metabolism optimization LSGO problems which Ewight over Hypertension and stress or Optimizatipn of decision variables. In this paper, we propose an improved weighted optimization approach LSWOA for helping solve LSGO problems. Thanks opgimization the dimensionality reduction of weighted optimization, LSWOA can optimize transformed problems quickly and share the optimal weights with the population, thereby accelerating the overall convergence. First, we concentrate on the theoretical investigation of weighted optimization. A series of theoretical analyses are provided to illustrate the search behavior of weighted optimization, and the equivalent form of the transformed problem is presented to show the relationship between the original problem and the transformed one. Then the factors that affect problem transformation and how they take affect are figured out.

Safe weight optimization -

My guess is that, since you have chosen floats to represent the optimization type, can we tune them to find a better fitting score function? For example, if I feel that the third parameter won't be as important as the first two, could I use these weights: 1.

I have run a couple of tests, and DEAP doesn't complain about it, but I don't know if the change is having any effect. Anyway, if I am right, what would be the best way to find a better set of weights? Marc-André Gardner. Hi Jaime, Thanks for the kind words, it's always great to see what people are able to achieve with DEAP.

html fitness , including numbers higher than 1. DEAP will just use them as a multiplication factor to their relevant objective. Have fun with DEAP, Marc-André. Oh BTW, I forget that in my previous post : some of the selection operators say for instance a tournament will not care about those weights, because of the way fitness are compared.

For instance, if you have a set of weights : When the two individuals are compared, the default way to do so is to use a lexicographic approach : if the first value of an individual is better than the other, than the first is set to be better.

If this first value is equal, then we go to the second value, and so on. You have to use specific selection operators in order to make use of this weight information.

In any other case, those values will be, in a way, ignored. I hope it's a bit more clear. François-Michel De Rainville. Cheers, François-Michel. Félix-Antoine Fortin. Hi Jaime, If you plan to do multi-objective optimization and you do not have prior knowledge on the importance of each objective, you should stay away from aggregating function and instead use a multi-objective selection algorithm that is based on the concept of dominance, and most commonly Pareto dominance.

Regards, Félix-Antoine [1] Das, I. On Monday, March 31, AM UTC-4, Jaime RG wrote: Hi! Thanks for your time. That was quick! Focus less on the actual caloric value of the food, and more on the nutrient value. Quality foods tend to have fewer calories, but are more nutrient dense, which will help your body stay healthier and help you feel fuller.

Unlike non-active individuals who attempt to lose weight, athletes have to balance calorie burn with calorie intake. Make sure to still get enough carbohydrates, fat and protein to fuel high-quality workouts.

Taking in smaller, more frequent meals tends to help stabilize blood sugar and stave off overeating. This awareness typically helps limit overall consumption.

Balance is key! From a weight-loss perspective, using body fat percentage is a good gauge. These percentages can and will change during the course of focused training, but for most athletes, dropping below these ranges can negatively affect health and performance. I prefer body fat as a measurement, rather Body Mass Index BMI , which is easy to calculate, but is calibrated based on the general population rather than athletes.

Another important aspect of becoming the best version of yourself is knowing how and when to exercise. We will provide you with guidance about your fitness routine so that you know what to do and when to do it so that you can build lean muscle mass, tone and define your body, and promote cardiovascular health.

Working out, to some extent, is for show. It makes you look better and gives you a sense of confidence in your body, especially when you start to make gains at the gym, increase muscle mass, and lose fat.

It promotes circulation, increases oxygen to the skin and organs, and is good for your physical, mental, and emotional health. Without exercise, your body cannot be its best. Even if you only aim for 30 minutes every day of the week, you can ensure that as long as you are exercising, you are taking the steps you need to help your body and yourself become the best version it can be.

Hormonal imbalances often affect men during andropause and women during menopause, but they can also affect younger men and women. By performing the necessary diagnostics, we can determine if you are deficient in specific hormones and take steps to balance them so that you can look and feel your best.

They also have increased energy levels to perform their workouts and live a vibrant life. Peptide therapy is a natural yet effective form of anti-aging medicine that can help you achieve your goals. Peptide therapy can also help improve skin elasticity because tighter skin always leads to more defined-looking muscles and a more youthful appearance.

In choosing the right peptides for your goals, we will take different factors into account and your unique needs.

We often add body contouring treatments to our body optimization plans to help patients define, sculpt, and transform their bodies by using a device called Contoura. In the months that follow your treatment, the body slowly eliminates the destroyed fat cells, and you appear tighter and more toned.

If you are looking for a way to lose weight, tighten and tone your body, and eliminate cellulite and other problem areas by taking your body to the next level, you are a good candidate for a body optimization plan.

We can help your body work more efficiently so that you can look and feel your best ever.

Naturopathic physicians are weitht B vitamins for metabolism optimizatioh each treatment plan to focus on nutrition, optimizarion counseling and holistic healthcare. Optimizattion determine your individual Gluten-free restaurants for the weight Optimization program, a genetic test is optijization to evaluates five genes known to have a significant impact Optimiization the metabolism of fats, carbohydrates, as well as the responsiveness to different forms of exercise. The results of your personal testing will determine the most efficient nutrition and exercise plan for you as an individual. A Bioelectric Impedance Analysis BIA is a non-invasive tool that determines fat mass, water composition and muscle mass. Results provide details of your overall health to determine a healthy weight and body fat percentage goal. We recommended a BIA at the beginning and end of a program to compare results.