Network Survivability Research Articles

Fuzzy Tree t -Spanners of Ratio Labelled Butterfly Network

Background/Objectives: The reliability and optimality of a communication system depend on the network that has a minimum distance. The minimum distance is the shortest path between the vertices. Even in error correction codes, the minimum Hamming distance ensures data integrity. Hence to have efficient routing protocols, the network is preferred to be a spanning tree that connects all the nodes of the communication system. As tree t- spanner is the optimization technique in determining minimum spanning tree, finding tree t-spanners of a ratio labelled fuzzy graph for the Butterfly network BF(n) is notion of this study. Methods: This study has introduced a new labelling called Ratio Labelling (RL) for examining the fuzziness in a crisp graph. Using the RL method, the crisp graphs were examined for the admittance of fuzziness and were thereby found, to have a tree t-spanners of ratio labelled fuzzy graphs. Findings: The quality of a network can be measured using the stretch factor of the network. The issues of network survival or link failures in a communication networks can be rectified by approximating the best flow spanner. As the optimal connectivity between the nodes is ensured by interconnection networks, the fuzzy tree t- spanners of undirected fuzzy Butterfly Network BF(n) are examined by labelling the vertices and edges using ratio labelling. The Breadth First Search (BFS) algorithm enhances the process of minimizing the spanning tree for an unweighted graph. Since the edge weightage is unique for all the edges of BF(n) under RL, searching for an edge with minimum weight by Kruskal’s algorithm or Prim’s algorithm are less meaningful. Hence, the BFS algorithm is adopted in finding the spanning tree T of Ratio Labelled BF(n). The bounds for the stretch factor t of a fuzzy tree t- spanner of ratio labelled BF(n) were obtained. Novelty: The literature survey shows that various classes of graphs were examined for the admissibility of tree t-spanners. Finding a tree t-spanner for a fuzzy graph was not found in literature, as per our knowledge. Extending the problem of finding a tree t -spanner of a ratio labelled fuzzy graph is an advancement in the field of fuzzy graphs. Keywords: Fuzzy Graph, Fuzzy distance, Ratio Labelling, Tree t- spanner, Butterfly Network

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

Machine learning‐based data fusion method for wireless sensor networks

AbstractFor wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non‐replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto‐Encoder (DSAE) model is constructed and trained using a layer‐wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto‐Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back‐Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

Thermal Hydraulic Analysis of Turbulent Flow of Superheated Steam Through Multiple Configurations of 3D Angular Piping Bend in Power Plant Installations

Abstract One of the most vulnerable parts in high-pressure and temperature pipeline networks in steam turbine power plant installations is pipe bends, due to intense fluctuation of velocity and pressure of the superheated steam flowing through the piping bend. On the other hand, because of unique chemical and physical properties, the transportation of superheated steam can adversely effect on the integrity of the pipe bends in the piping network. Therefore, the potential risk of pipeline failure becomes more probable at bends. Therefore, to ensure the survivability of the piping network, thorough investigations of the superheated steam flowing through pipe bends are of great significance in better understanding its flow behavior. However, to predict flow behavior at piping bend, intensive research by direct experiment at high temperature and pressure might not be possible to some extent. In that case, computer codes and models can help us analyze flow behavior of superheated steam at pipe bends. The current work is a computational fluid dynamics investigation, where numerical examination is carried out by commercial code ansys fluent for thermal hydraulic analysis of flow behavior of superheated steam through multiple configurations of 3D angular piping bend: 90 deg, 120 deg, and 135 deg. Validation of credibility of the computational approach is done against Sudo et al.'s experiment. With relatively good agreement between the experiment and numerical result using air flowing through 90 deg elbow, single-phase turbulent flow of superheated steam is examined by the application of realizable turbulence model (k–ɛ). The final assessment of the computed results has been done using qualitative and quantitative analyses. The study reveals that unlike the pressure profiles, the radial velocity profiles at the bend exit are noticeably different from those at the bend inlet, since they exhibit more rounded peaks and gentler velocity gradients. The study also shows that the radial pressure and velocity profiles do not display any sign of the existence of a pair of vortices.

Comparative study of robust optimization model of multimaterial emergency logistics in an uncertain environment

AbstractGiven the significance of objective functions in alleviating human suffering within the humanitarian relief supply chain, it is imperative to consider both logistics costs and the cost of deprivation as ideal targets. This study investigates a multimaterial single‐cycle stochastic mixed integer model, integrating uncertainties about post‐disaster demand, supply survival, and road networks. An innovative heuristic approach is used to solve the stochastic facility location problem, which minimizes unfulfilled requests and response times in lexicographic order. This research also compares the cost‐minimization model of the pre‐positioning problem to an alternative model that maximizes demand satisfaction under resource constraints. Empirical validation through numerous case studies and solutions confirms the effectiveness and feasibility of the alternative model. The results of our experiments consistently demonstrate that the best solution provided by the alternative model not only satisfies the minimum cost model but also surpasses the conventional problem, without compromising performance. Consequently, it is crucial to avoid the cost‐minimization approach at the expense of human suffering.

ELA-RCP: An energy-efficient and load balanced algorithm for reliable controller placement in software-defined networks

Software-defined networking is a modern and popular network technology that utilizes controllers as network operating systems to manage the network and allow user applications to interact with network hardware. Determining the optimal number of controllers and the optimal place to install them are some of the multi-controller model challenges that are known as the Controller Placement Problem (CPP). CPP is an optimization problem that tries to minimize delay and achieve a balanced network. CPP becomes more important when we intend to increase reliability and reduce energy consumption. This paper proposes an Energy-efficient and Load-balanced optimization Algorithm for Reliable Controller Placement (ELA-RCP) in Software-Defined Networks (SDN). The proposed ELA-RCP is a three-step solution to CPPs. First, it determines the optimal number of controllers using the proposed cellular learning automata, taking into account network traffic status, scale, and budget. Second, an innovative meta-heuristic algorithm is proposed to identify proper controller locations based on factors like path reliability, controller processing capacity, propagation delay, and energy consumption. In the last step, load balancing and the reliability of the controllers are improved using queuing theory and a proposed greedy algorithm. ELA-RCP is evaluated with real-world topologies. The simulation demonstrates up to 25% improvement in energy consumption, up to 108% improvement in the network survivability, and up to 68% improvement in mean average delay rise compared to particle swarm optimization, firefly optimization algorithm, ant lion optimization, and seagull optimization algorithm.

Can "Fire" change medicinal effects? Processing heat transforms the efficacy of Chaihu from antipyretic to hepatic-soothing

BackgroundTraditional Chinese Medicine (TCM) from centuries past asserts that unprocessed Chaihu (Bupleurum chinense DC.) primarily exhibits exterior-releasing properties, hepatic soothing, and is advisable for use in cases of Yin deficiency and bone-steaming sensation. To validate this age-old clinical experience, we employed modern biomedical methodologies such as TCM network pharmacology and molecular docking. MethodsThrough network pharmacology research, specific targets (AKT, IKKs, IκBα, CDK1, VEGKA) associated with Chaihu in treating diseases were identified within the PI3K-Akt signaling pathway. Initial conclusions were drawn through target network survival validation, network immunohistochemistry verification, and molecular docking studies, suggesting that processing Chaihu with heat alters its efficacy. Subsequent validation using PCR and WB experiments on unprocessed Chaihu, turtle blood-processed Chaihu, vinegar-processed Chaihu, and Quercetin against the selected PI3K-Akt signaling pathway further confirmed that processing Chaihu with heat enhances its inhibitory effect on the PI3K-Akt signaling pathway. ResultsThis interference aids in transcriptional regulation of inflammatory factors, thereby augmenting the hepatic soothing effects, particularly enhancing the hepatic soothing and depression-relieving efficacy post-processing. Additionally, turtle blood-processed Chaihu and vinegar-processed Chaihu exhibited a more pronounced impact on AKT and IKKs compared to unprocessed Chaihu. Conclusions: This substantiates that processing Chaihu with heat weakens its exterior-releasing and antipyretic effects while strengthening its hepatic soothing and depression-relieving properties.

MAC-DBA based new cost-efficient architectures of network congestion-aware and survivable fiber-wireless and FTTx networks

In this paper, we propose two novel architectures for the short-reach optical access network (SR-OAN) and long-reach optical access network (LR-OAN) to provide high bandwidth and high connection availability to the users in wireless and fiber-to-the-x networks. The proposed architectures are referred to as network congestion alleviating architecture with partial protection (NCAA-PP) and network congestion alleviating architecture with full protection (NCAA-FP). Both the proposed NCAA-PP and NCAA-FP architectures utilize the idle backup optical line terminals and dark fibers efficiently to develop the integrated mechanisms for alleviating network congestion and providing protection against failures of the optical resources in the network. We also propose a dynamic bandwidth allocation scheme, referred to as a surplus bandwidth allocation scheme, at medium access control layer to allocate surplus bandwidths to the users in the SR-OAN and LR-OAN during the network congestion periods. For the practical network scenarios, the simulation and analytical outcomes reveal that the proposed NCAA-PP and NCAA-FP architectures can reduce up to 82% average packet delay during the network congestion periods. Moreover, the proposed architectures offer high connection availabilities to the users and are very cost-efficient.

Dynamic survivability of two-layer networks: The role of interlayer coupling

The study investigates the dynamic survivability of dynamic networks on the basis of two-layer Erdös-Rényi random graph (ER) and Barabási-Albert scale-free (BA) networks with three interlayer coupling patterns. We consider the synchronization for the key mission to complete and attack edges both randomly and deliberately. The study provides a detailed analysis demonstrating that the dynamic survivability of two-layer networks is influenced by interlayer coupling schemes, resulting in either an increase or decrease compared to single-layer networks. Furthermore, we show the impact of network parameters and attack strategies on the dynamic survivability of two-layer networks, and highlighting the significance of intralayer links. The findings provide us new insights into the protection of complex network systems from collapse.

In silico Study of Clinical Prognosis Associated MicroRNAs for Patients with Metastasis in Clear Cell Renal Carcinoma

Background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. Objective: This study aims to propose a computational research framework that hypothesizes that a set of miRNAs functions as key regulators in modulating gene expression networks of kidney cancer survival. Methods: We retrieved the NGS data from the TCGA-KIRC extracted from UCSC Xena. A set of prognostic miRNAs was acquired through multiple Cox regression analyses. We adopted machine learning approaches to evaluate miRNA prognosis's classification performance between normal, primary (M0), and metastasis (M1) samples. The molecular mechanism between primary cancer and metastasis was investigated by identifying the regulatory networks of miRNA's target genes. Results: A total of 14 miRNAs were identified as potential prognostic indicators. A combination of high-expression miRNAs was associated with survival probability. Machine learning achieved an average accuracy of 95% in distinguishing primary cancer from normal tissue and 79% in predicting the metastasis from primary tissue. Correlation analysis of miRNA prognostics with target genes unveiled regulatory network disparities between metastatic and primary tissues. Conclusion: This study has identified 14 miRNAs that could potentially serve as vital biomarkers for diagnosing and prognosing ccRCC. Differential regulatory networks between metastatic and primary tissues in this study provide the molecular basis for assessment and therapeutic treatment for ccRCC patients.

Dynamic survivability of two-layer networks with different topologies

Dynamic survivability of two-layer networks with different topologies

Control of Telecommunication Network Parameters under Conditions of Uncertainty of the Impact of Destabilizing Factors

The classification of the natural and anthropogenic destabilizing factors of a telecommunications network as a complex system is presented herein. This research shows that to evaluate the parameters of a telecommunications network in the presence of destabilizing factors, it is necessary to modify classical linear methods to reduce their sensitivity to the incompleteness of a priori information. Using generalized linear models of multiple regression, a combined method was developed for assessing and predicting the survivability of a telecommunications network under conditions of uncertainty regarding the influence of destabilizing factors. The method consists of accumulating current information about the parameters and state of the network, the statistical analysis and processing of information, and the extraction of sufficient sample statistics. The basis of the developed method was balancing multiple correlation–regression analysis with the number of regression equations and the observed results. Various methods of estimating the mathematical expectation and correlation matrix of the observed results under the conditions of random loss of part of the observed data (for example, removing incomplete sample elements, substituting the average, pairwise crossing out, and substituting the regression) were analyzed. It was established that a shift in the obtained estimates takes place under the conditions of a priori uncertainty of the statistics of the observed data. Given these circumstances, recommendations are given for the correct removal of sample elements and variables with missing values. It is shown that with significant unsteadiness of the parameters and state of the network under study and a noticeable imbalance in the number of regression equations and observed results, it is advisable to use stepwise regression methods.

Inhibition of CSF1R and KIT With Pexidartinib Reduces Inflammatory Signaling and Cell Viability in Endometriosis.

Endometriosis is a common and debilitating disease, affecting ∼170 million women worldwide. Affected patients have limited therapeutic options such as hormonal suppression or surgical excision of the lesions, though therapies are often not completely curative. Targeting receptor tyrosine kinases (RTKs) could provide a nonhormonal treatment option for endometriosis. We determined that 2 RTKs, macrophage-colony stimulating factor 1 receptor (CSF1R) and mast/stem cell growth factor receptor KIT (KIT), are overexpressed in endometriotic lesions and could be novel nonhormonal therapeutic targets for endometriosis. The kinase activity of CSF1R and KIT is suppressed by pexidartinib, a small molecule inhibitor that was recently approved by the US Food and Drug Administration. Using immunohistochemistry, we detected CSF1R and KIT in endometriotic tissues obtained from peritoneal lesions, colorectal lesions, and endometriomas. Specifically, we show that KIT is localized to the epithelium of the lesions, while CSF1R is expressed in the stroma and macrophages of the endometriotic lesions. Given the high epithelial expression of CSF1R and KIT, 12Z endometriotic epithelial cells were used to evaluate the efficacy of dual CSF1R and KIT inhibition with pexidartinib. We found that pexidartinib suppressed activation in 12Z cells of JNK, STAT3, and AKT signaling pathways, which control key proinflammatory and survival networks within the cell. Using quantitative real-time polymerase chain reaction, we determined that pexidartinib suppressed interleukin 8 (IL8) and cyclin D1 (CCND1) expression. Lastly, we demonstrated that pexidartinib decreased cell growth and viability. Overall, these results indicate that pexidartinib-mediated CSF1R and KIT inhibition reduces proinflammatory signaling and cell viability in endometriosis.

ANGPT1 promotes M1 macrophage polarization and inhibits lung adenocarcinoma progression by inhibiting the TGF-β signalling pathway.

Immune cells in the immune microenvironment of lung adenocarcinoma (LUAD) are involved in tumour progression. The aim of this study was to investigate the molecular mechanisms of immune infiltration-related genes in LUAD. The GEO, GeneCards, BioGPS and Genehopper databases were utilized to screen for immune infiltration-related differentially expressed genes (DEGs) in LUAD. Protein-protein interaction (PPI) network construction and survival analysis were performed in the Kaplan-Meier database to identify hub genes. The TIMER 2.0 database was used to analyse the correlations between hub gene expression and immune infiltration level. Co-culture of LUAD cells with macrophages and plasmid transfection to overexpress ANGPT1 were performed to investigate the function of the hub genes in LUAD using RT-qPCR, Western blot, CCK-8 assays, cell wound healing assays and transwell assays. A total of 88 immune infiltration-related DEGs were screened. The hub genes ANGPT1, CDH5 and CLDN5 were reduced in LUAD, while COL3A1 was overexpressed. ANGPT1 was significantly correlated with OS, FP and PPS, and ANGPT1 promoted the polarization of M1 macrophages. Further experiments revealed that ANGPT1 inhibited the proliferation, migration and invasion of LUAD cells by inhibiting the TGF-β signalling pathway. ANGPT1 promotes polarization of M1 macrophages and reduces the progression of LUAD by inhibiting the TGF-β signalling pathway. Thus, ANGPT1 could be employed as a predictive biomarker and immunotherapy target for lung cancer.

Transcription factor ZEB1 coordinating with NuRD complex to promote oncogenesis through glycolysis in colorectal cancer.

Colorectal cancer (CRC) is an aggressive primary intestinal malignancy with the third-highest incidence and second-highest mortality among all cancer types worldwide. Transcription factors (TFs) regulate cell development and differentiation owing to their ability to recognize specific DNA sequences upstream of genes. Numerous studies have demonstrated a strong correlation between TFs, the etiology of tumors, and therapeutic approaches. Here, we aimed to explore prognosis-related TFs and comprehend their carcinogenic mechanisms, thereby offering novel insights into the diagnosis and management of CRC. Differentially expressed TFs between CRC and normal tissues were identified leveraging The Cancer Genome Atlas database, Weighted correlation network analysis and Cox regression analysis were performed to identify prognosis-related TFs. The cellular functions of hub TF zinc finger E-box binding homeobox 1 (ZEB1) were determined using by 5-ethynyl-2'-deoxyuridine and cell invasion assays in CRC cells. RNA-sequencing, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses were used to identify the cellular processes in which ZEB1 participates. Immunoaffinity purification, silver staining mass spectrometry, and a chromatin immunoprecipitation assay were conducted to search for proteins that might interact with ZEB1 and the target genes they jointly regulate. Thirteen central TFs related to prognosis were identified through bioinformatics analysis techniques. Among these TFs, ZEB1 emerged as the TF most closely associated with CRC, as determined through a combination of regulatory network diagrams, survival curves, and phenotype analyses. ZEB1 promotes CRC cell growth by recruiting the NuRD(MTA1) complex, and the ZEB1/NuRD(MTA1) complex transcriptionally represses glycolysis-associated tumor suppressor genes. Our study not only identified a hub biomarker related to CRC prognosis but also revealed the specific molecular mechanisms through which ZEB1 affects cancer progression. These insights provide crucial evidence for the diagnosis of CRC and potential treatment opportunities.

Pan-cancer analysis revealing the multidimensional expression and prognostic and immunologic roles of TGFB1 in cancer.

This study aimed to perform an integrated pan-cancer analysis to characterize the expression patterns, prognostic value, genetic alterations, and immunologic roles of transforming growth factor beta 1 (TGFB1) across diverse human cancer types. Bioinformatics analyses were conducted using multiple public databases including The Cancer Genome Atlas, Genotype-Tissue Expression, Clinical Proteomic Tumor Analysis Consortium, TIMER2, GEPIA2, cBioPortal, StringDB, and others. Differential expression, survival, immune correlation, and protein interaction network analyses were performed. TGFB1 was overexpressed in several tumor types compared with that in normal tissues. High TGFB1 expression was associated with an advanced stage and poorer prognosis in certain cancers. TGFB1 mutations occurred in 1.3% of 10,967 cases surveyed. TGFB1 expression correlated with tumor-infiltrating immune cells and immunotherapy-related genes. This comprehensive multi-omics analysis revealed the complex expression and prognostic landscape of TGFB1 across cancers. TGFB1 is emerging as a potential immunotherapeutic target in certain contexts. Further research should elucidate its multifaceted tumor-promoting and tumor-suppressive mechanisms. Our pan-cancer analysis provides new insights into TGFB1 as a prognostic biomarker and immunotherapeutic target in human cancers, and our findings may guide future preclinical and clinical investigations of TGFB1-directed therapies.

Survivable Payment Channel Networks

Survivable Payment Channel Networks

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis.

The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set. For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone. Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach

This paper presents a deep learning (DL) approach for predicting survival probabilities of renal cancer patients based solely on preoperative CT imaging. The proposed approach consists of two networks: a classifier- and a survival- network. The classifier attempts to extract features from 3D CT scans to predict the ISUP grade of Renal cell carcinoma (RCC) tumors, as defined by the International Society of Urological Pathology (ISUP). Our classifier is a 3D convolutional neural network to avoid losing crucial information on the interconnection of slides in 3D images. We employ multiple procedures, including image augmentation, preprocessing, and concatenation, to improve the performance of the classifier. Given the strong correlation between ISUP grading and renal cancer prognosis in the clinical context, we use the ISUP grading features extracted by the classifier as the input to the survival network. By leveraging this clinical association and the classifier network, we are able to model our survival analysis using a simple DL-based network. We adopt a discrete LogisticHazard-based loss to extract intrinsic survival characteristics of RCC tumors from CT images. This allows us to build a completely parametric survival model that varies with patients' tumor characteristics and predicts non-proportional survival probability curves for different patients. Our results demonstrated that the proposed method could predict the future course of renal cancer with reasonable accuracy from the CT scans. The proposed method obtained an average concordance index of 0.72, an integrated Brier score of 0.15, and an area under the curve value of 0.71 on the test cohorts.

Implementation of survivability aware protocols in WSN for IoT applications using Contiki-OS and hardware testbed evaluation

The Internet of Things is a network of devices capable of operating and communicating individually and working for a specific goal collectively. Technologically, many networking and computing mechanisms have to work together with a common objective for the IoT applications to function, and many sensing and actuating devices have to get connected to the Internet backbone. The networks of resource-constrained sensor devices constitute an integral part of IoT application networks. Network survivability is a critical aspect to consider in the case of a network of low-power, resource-constrained devices. Algorithms at different layers of the protocol stack have to work collectively to enhance the survivability of the application network. In this article, the survivability-aware protocols for wireless sensor networks for IoT applications are implemented in real network scenarios. The routing strategy, Survivable Path Routing protocol, and the channel allocation technique, Survivability Aware Channel Allocation, are implemented in Contiki-OS, the open-source operating system for IoT. Furthermore, the implementation scenarios are tested with the FIT IoT Lab hardware testbed. Simulated results are compared with the results obtained from the testbed evaluation.