Path Recognition of the Regional Education Expansion Based on Improved Dragonfly Algorithm

To solve the problems of low recognition rate, high misrecognition rate, and long recognition time, the path recognition method of the regional education scale expansion based on the improved dragonfly algorithm is proposed. Through a variety of different behaviors utilized in the optimization process, the dragonfly algorithm model has been constructed. The step size and the position vector are introduced to update the dragonfly’s location. The dragonfly’s foraging behaviors are accurately simulated. Afterward, the dragonfly algorithm is combined with the flower authorization algorithm. The conversion probability is added, and the dragonfly’s global development ability is adjusted in real-time. Then, the dragonfly algorithm is improved. The improved dragonfly algorithm is employed to extract the features of the expansion path of the regional education scale. The improved support vector machine is utilized as a classifier to realize the recognition of the regional education scale expansion path. The experimental results denote that the proposed method has a high recognition rate of the regional education scale expansion path and can effectively reduce the misrecognition rate and shorten the recognition time.

Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation

PurposeAccurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.MethodsFirst, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.ResultsIn this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors’ manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.ConclusionS-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.

LEAST COST DIETS FOR PRODUCTION. III. REGRESSION ANALYSIS AND ECONOMIC ASPECTS OF THE WEANER DIETS

This paper represents the third in the series of designed to explore the scope and contributions of computerized diets to the overall improvements in the technical and economic efficiency in the pig industry. The present paper attempts to establish the functional relationships between the principal nutrient components (Protein and energy) of weaners’ diets on weight gain, feed utilization and carcass quality. The established functional relationship was used as a basis for incorporating animal performance into feed formulation activities while using linear programming techniques to derive expansion path diets. Cumulative feed intake was positively related to liveweight gain in weaner pigs. While protein and energy intakes are significant explanatory variable, they explained only 37 and 36 percent, respectively, of the total variations in liveweight gain. No significant relationship was found between carcass quality and protein and energy intake levels for pigs of the weight range studied. While it was clearly demonstrated that the formulation of expansion path diets using liner programming techniques could be used for selecting cost diets to achieve different weekly liveweight gains, its practical application must await result of animal feeding trials designed to test the performance of the derived expansion path diets

A Look into the Economics behind Cancer Interventions and Drug Development

Cancer is an extraordinarily tough combatant and is quickly becoming the number one cause of death in the world. With the global economic cost of cancer accumulating to $1.16 trillion in 2010, something has to be done to decrease this financial and societal weight that’s suffocating humanity. Through cost-effective analysis, it was found that cervical cancer interventions were the most cost-effective given their inclusion of advantageous preventative strategies at low costs. By implementing preventative measures, using a step-wise approach to treatment as dictated by the expansion path, and intervening at the earliest stages of cancer provide the most cost-effective outcomes. With revenues for pharmaceutical companies exceeding their research and development costs by potentially ten-fold only adds fuel to the fire on the drug pricing debate. Through cost-effective treatment of cancer and increased competition amongst pharmaceutical firms developing oncologic drugs to lower prices and increase patient access, the burden of cancer can begin to shrink.

COVID-19: Finding the End Day

The study has pivoted on finding a methodology to forecast the end day of the menace of Coronavirus Disease of 2019 (COVID-19) or such pandemic that the planet faces on and often, challenging the core of the civilization. This model has resort to an indirect method to find the end day. As the pandemic grows exponentially, the rate of growth of total cases over previous day reduces asymptotically with herd immunity gaining strength to strength. Instead of finding flat head of the exponential expansion path, the model has looked into close to zero value of daily growth rate to find the end day. ARIMA (p,q,r) model for data smoothing and exponential trend line methodology adopted to find the end day. COVID-19 data for 63 days from March 20, 2020 to May 21, 2020 for seven countries and the globe explored with the proposed methodology. The study has projected toll of COVID-19 using a continuous constant exponential growth/decay model. The end day of the pandemic is projected for the globe when the expansion of the disease would be 0.01% per day. The methodology can be improved further by inclusion of other parameters of social and virology implications.

Assessing Cogeneration Activity in Extraction–Condensing Steam Turbines: Dissolving the Issues by Applied Thermodynamics

Extraction–condensing steam turbines mix cold-condensing and cogeneration activities making the respective power and fuel flows not directly observable. A flawed assessment of the flows is causing confusion and bias. A steam expansion path on a Mollier diagram reveals the design characteristics of a thermal power plant and of its embedded combined heat and power (CHP) activities. State variable data on a unit mass of steam, entering the turboset as life steam and leaving it at one of the heat extraction exhausts, provide the roster of the power-heat production possibility set of the plant. The actual production possibilities are drawn from the roster by applying capacity data and constraints on the heat extraction points. Design power-to-heat ratios of CHP activities are univocally identified, allowing accurate assessments of cogenerated power. This information is needed for proper incentive regulation of CHP activities, pursuing maximization of CHP quality and quantity. Quality is gauged by the power-to-heat ratio, principally a design (investment) decision. Quantity is gauged by the operational amounts of recovered heat exhausts. Optimal regulatory specificity is attained through setting generic frameworks by technology, accommodating investment and operational decisions by plant owners. Our novel method is explained and applied with numerical data, also revealing the flaws in present regulations.