Internet of Things (IoT) is a new revolution of the Internet, which can be thought of as an uprising expansion of internet services. IoT is defined in many different ways. It covers many areas of life, including house...
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Internet of Things (IoT) is a new revolution of the Internet, which can be thought of as an uprising expansion of internet services. IoT is defined in many different ways. It covers many areas of life, including houses, cities, automobiles, and roads. It also includes devices that track people's behavior and utilize the information gathered for push services. Any object may access the web over a wired or wireless network with the IoT. In addition to authentication and security, extensive research and development have gone into energy awareness. Mobile Internet of Things (MIoT) is the following stage in this situation. Mobile data collecting, data analysis, energy management, security and privacy, and the provision of IoT services are some issues arising when using MIoT. So, this paper proposed an energy-aware technique for resource allocation in MIoT. Due to this problem's NP-hard nature, a new approach is suggested to reduce the network's energy consumption and end-to-end delay in MIoT using selfish node ranking and ant colony optimization algorithms. The proposed method is compared to Whale Optimization algorithm (WOA) and Task Priority-based Resource Allocation (TPRA) algorithms. The findings show the network's substantial changes in energy consumption and end-to-end delay using the proposed method. The findings of the current work are significant for academics and offer insights into upcoming study areas in this subject.
Stroke is a common disease characterized by high disability rate and high mortality rate. Accurate detection and continuous monitoring are vital for the treatment of stroke. As a promising medical imaging technique, e...
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Stroke is a common disease characterized by high disability rate and high mortality rate. Accurate detection and continuous monitoring are vital for the treatment of stroke. As a promising medical imaging technique, electrical impedance tomography (EIT) is able to provide an alternative for brain imaging. With this technique, conductivity distribution variation caused by pathological change can be visualized. However, image reconstruction is a severely ill-posed inverse problem. Particularly in brain imaging, irregular and multi-layered head structure along with low-conductivity skull further aggravate the challenge for accurate reconstruction. To solve this problem, a novel image-reconstruction method based on modified tuna swarm optimization is proposed for visualizing the conductivity distribution in brain EIT. To evaluate the performance of the proposed method, extensive simulations are carried out on a three-layer head model. The anti-noise performance of the proposed method is estimated by considering noise with different signal-to-noise ratios. In addition to simulation, phantom experiments are conducted to further verify the effectiveness of the proposed method. Both reconstructed images and quantitative evaluations demonstrate that the proposed approach performs well in the reconstruction of simulated intracerebral hemorrhage and secondary ischemia. This work would offer an alternative for accurate reconstruction in medical imaging based on EIT.
Accurate prediction of drug susceptibility is one of themost important steps in personalized *** of machine learning to pharmacogenomic data for sensitivity prediction can help study the mechanism of drug response and...
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Accurate prediction of drug susceptibility is one of themost important steps in personalized *** of machine learning to pharmacogenomic data for sensitivity prediction can help study the mechanism of drug response and find more effective anti-tumor drugs.
This study presents a novel approach to kidney stone diagnosis using convolutional neural networks (CNN), applied to computed tomography (CT) images. The research addresses the challenge of data imbalance and protocol...
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This study presents a novel approach to kidney stone diagnosis using convolutional neural networks (CNN), applied to computed tomography (CT) images. The research addresses the challenge of data imbalance and protocol variation in medical imaging, which often leads to poor generalization of deep learning models. The model first uses three preprocessing techniques to enhance the quality of raw images and increase their quantity for effective CNN training. The main idea is to optimize the main arrangement of the convolutional neural network based on the proposed flexible version of dwarf mongoose optimization (FDMO) algorithm to provide a good detector model in kidney stone diagnosis. The model is then trained and tested on images from "CT Kidney Dataset", and its comparison results with some other published works demonstrates its robustness and ability to generalize. The results indicate a significant improvement in diagnostic accuracy, potentially minimizing physician-induced errors and enhancing patient care.
The present study models the multi-material topology optimization problems as the multi-valued integer programming (MVIP) or named as combinatorial optimization. By extending classical convex analysis and convex progr...
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The present study models the multi-material topology optimization problems as the multi-valued integer programming (MVIP) or named as combinatorial optimization. By extending classical convex analysis and convex programming to discrete point-set functions, the discrete convex analysis and discrete steepest descent (DSD) algorithm are introduced. To overcome combinatorial complexity of the DSD algorithm, we employ the sequential approximate integer programming (SAIP) to explicitly and linearly approximate the implicit objective and constraint functions. Considering the multiple potential changed directions for multi-valued design variables, the random discrete steepest descent (RDSD) algorithm is proposed, where a random strategy is implemented to select a definitive direction of change. To analytically calculate multimaterial discrete variable sensitivities, topological derivatives with material contrast is applied. In all, the MVIP is finally transferred as the linear 0-1 programming that can be efficiently solved by the canonical relaxation algorithm (CRA). Explicit nonlinear examples demonstrate that the RDSD algorithm owns nearly three orders of magnitude improvement compared with the commercial software (GUROBI). The proposed approach, without using any continuous variable relaxation and interpolation penalization schemes, successfully solves the minimum compliance problem, strength-related problem, and frequency-related optimization problems. Given the algorithm efficiency, mathematical generality and merits over other algorithms, the proposed RDSD algorithm is meaningful for other structural and topology optimization problems involving multivalued discrete design variables.
Background:Tumor-stroma percentage(TSP)is a prognostic risk factor in numerous solid *** this,the prognostic significance of TSP in gastric cancer(GC)remains *** the development of a personalized predictive model and ...
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Background:Tumor-stroma percentage(TSP)is a prognostic risk factor in numerous solid *** this,the prognostic significance of TSP in gastric cancer(GC)remains *** the development of a personalized predictive model and a semi-automatic identification system,our study aimed to fully unlock the predictive potential of TSP in ***:We screened GC patients from Shanghai General Hospital(SGH)between 2012 and 2019 to develop and validate a *** and multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic factors influencing the prognosis for GC *** nomogram was further validated externally by using a cohort from Bengbu Medical College(BMC).All patients underwent radical gastrectomy,with those diagnosed with locally advanced GC receiving adjuvant *** primary outcome measured was overall survival(OS).The semi-automatic identification of the TSP was achieved through a computer-aided detection(CAD)system,denoted as TSP-cad,while TSP identified by pathologists was labeled as ***:A total of 813 GC patients from SGH and 59 from BMC were enrolled in our ***-visual was identified as an adverse prognostic factor for OS in GC and was found to be associated with pathological Tumor Node Metastasis staging system(pTNM)stage,T stage,N stage,perineural invasion(PNI),lymphovascular invasion(LVI),TSP-visual,tumor size,and other *** Cox regression using the training cohort revealed that TSP-visual(hazard ratio[HR],2.042;95%confidential interval[CI],1.485-2.806;P<0.001),N stage(HR,2.136;95%CI,1.343-3.397;P=0.010),PNI(HR,1.791;95%CI,1.270-2.526;P=0.001),and LVI(HR,1.482;95%CI,1.021-2.152;P=0.039)were independent *** factors were incorporated into a novel nomogram,which exhibited strong predictive accuracy for 5-year OS in the training,internal validation,and external validation cohorts(area under the curve?0.744,0.759,and 0.8
With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solutio...
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With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single -operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. Based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching -learning -based optimization) hybrid K -means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high -similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching -learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency.
Developing organic light-emitting diodes (OLEDs) with a desired emission color and efficiency involves complex efforts in material selection and optimizing the device structure due to their multilayered architectures....
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Developing organic light-emitting diodes (OLEDs) with a desired emission color and efficiency involves complex efforts in material selection and optimizing the device structure due to their multilayered architectures. Notably, the cavity structure in the OLEDs allows for a wide range of emission colors and efficiencies based on the thicknesses and optical constants of the layers, even within a specific material set. Conventional approaches to achieving optimized OLED designs can prove to be financial-, labor-, and time-intensive for researchers, considering the multitude of combinations necessary for the complex, multilayered structure. To address these challenges, this study introduces a novel machine learning (ML) algorithm capable of intelligently predicting the ideal device structure for OLEDs, considering organic layer thicknesses and refractive indexes. The rule-based ML algorithm exhibits impressive accuracy, with an error margin of less than 0.5% for red-, green-, and blue-emitting OLEDs. These findings emphasize the potential of the ML algorithm as an invaluable solution to streamline the process of obtaining optimized OLED designs, offering substantial time and resource savings with high precision.
The problem of multiway partitioning of an undirected graph is considered. A spectral method is used, where the k > 2 largest eigenvalues of the normalized adjacency matrix (equivalently, the k smallest eigenvalues...
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The problem of multiway partitioning of an undirected graph is considered. A spectral method is used, where the k > 2 largest eigenvalues of the normalized adjacency matrix (equivalently, the k smallest eigenvalues of the normalized graph Laplacian) are computed. It is shown that the information necessary for partitioning is contained in the subspace spanned by the k eigenvectors. The partitioning is encoded in a matrix \Psi in indicator form, which is computed by approximating the eigenvector matrix by a product of \Psi and an orthogonal matrix. A measure of the distance of a graph to being k-partitionable is defined, as well as two cut (cost) functions, for which Cheeger inequalities are proved;thus the relation between the eigenvalue and partitioning problems is established. Numerical examples are given that demonstrate that the partitioning algorithm is efficient and robust.
Recently the interval forecasting of carbon price is investigated by advanced research since it can better quantify the uncertainty and reliability of the forecast value in comparison with point forecasting. However, ...
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Recently the interval forecasting of carbon price is investigated by advanced research since it can better quantify the uncertainty and reliability of the forecast value in comparison with point forecasting. However, this kind of model is always limited to the distribution-based method, which can only conduct symmetric prediction intervals and heavily rely on accurate point prediction and preprogrammed errors' probability distribution. Therefore, we make the following improvements for enriching research on the interval prediction of carbon price and providing more references for researchers in related fields. Firstly, lower upper bound estimation model (LUBE) is employed to conduct the prediction interval rather than the distribution-based method, and this designer can generate asymmetric upper and lower bounds without the need for assumptions and humanly devised parameters. Secondly, the causal inference is applied to the feature selection rather than the correlation analysis, which can well improve the generalization ability of prediction model. Thirdly, we improved the objectives when using multi-objective evolutionary algorithms for searching more efficient solutions in less time. Finally, through various experiments the most effective combination of LUBE and the ensemble method are verified, which are also proved by experiments.
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