Location method for emergency rescue node on expressways based on spatio-temporal characteristics of vehicle operation.

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

High-confidence structural annotation of substances via multi-layer molecular network reveals the system-wide constituent alternations in milk interfered with diphenylolpropane.

The spectral database-based mass spectrometry (MS) matching strategy is versatile for structural annotating in ingredient fluctuation profiling mediated by external interferences. However, the systematic variability of MS pool attributable to aliasing peaks and inadequacy of present spectral database resulted in a substantial metabolic feature depletion. An amended procedure termed multiple-charges overlap peaks extraction algorithm (MCOP) was proposed involving identifying collision-trigged dissociation precursor ions through iteratively matching mass features of fragmentations to expand the spectral reference library. We showcased the versatility and utility of established strategy in an investigation centered on the stimulation of milk mediated by diphenylolpropane (BPA). MCOP enabled efficient unknown annotations at metabolite-lipid-protein level, which elevated the accuracy of substance annotation to 85.3% after manual validation. Arginase and α-amylase (|r| > 0.75, p < 0.05) were first identified as the crucial issues via graph neural network-based virtual screening in the abnormal metabolism of urea triggered by BPA, resulting in the accumulation of arginine (original: 1.7 µg kg-1 1.7 times) and maltodextrin (original: 6.9 µg kg-1 2.9 times) and thus, exciting the potential dietary risks. Conclusively, MCOP demonstrated generalisation and scalability and substantially advanced the discovery of unknown metabolites for complex matrix samples, thus deciphering dark matter in multi-omics.

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN.

Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios.

Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced.

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems.

Local Positioning Systems (LPS) have shown excellent performance for applications that demand high accuracy. They rely on ad-hoc node deployments which fit the environment characteristics in order to reduce the system uncertainties. The obtainment of competitive results through these systems requires the solution of the Node Location Problem (finding the optimal cartesian coordinates of the architecture sensors). This problem has been assigned as NP-Hard, therefore a heuristic solution is recommended for addressing this complex problem. Genetic Algorithms (GA) have shown an excellent trade-off between diversification and intensification in the literature. However, in Non-Line-of-Sight (NLOS) environments in which there is not continuity in the fitness function evaluation of a particular node distribution among contiguous solutions, challenges arise for the GA during the exploration of new potential regions of the space of solutions. Consequently, in this paper, we first propose a Hybrid GA with a combination of the GA operators in the evolutionary process for the Node Location Problem. Later, we introduce a Memetic Algorithm (MA) with a Local Search (LS) strategy for exploring the most different individuals of the population in search of improving the previous results. Finally, we combine the Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA), designing an enhanced novel methodology for solving the Node Location Problem, a Hybrid Memetic Algorithm (HMA). Results show that the HMA proposed in this article outperforms all of the individual configurations presented and attains an improvement of 14.2% in accuracy for the Node Location Problem solution in the scenario of simulations with regards to the previous GA optimizations of the literature.

The relation between oncologic outcomes and metastatic lymph node location following laparoscopic resection of stage III colon cancer.

PURPOSE: Colon cancers are staged by assessing more than 12 lymph nodes, but there is still a controversy over the number of lymph nodes. Only a few studies of metastatic lymph node position in colon cancer have been published with its significance not completely understood. This study aimed to compare survival rates according to metastatic lymph node position following radical lymph node dissection for stage III colon cancers. METHODS: This retrospective study evaluated data prospectively collected at a tertiary teaching hospital from 349 patients who underwent laparoscopic colectomy with radical node dissection between December 2009 and December 2014. Lymph nodes were numbered and classified into lymph node metastasis (LNM) groups LNM1, LNM2, and LNM3 and their short- and long-term outcomes were compared. RESULTS: The LNM1, LNM2, and LNM3 groups included 229, 94, and 26 patients, respectively. Patient characteristics differed by locations (p < 0.001). A mean 34.6 lymph nodes were harvested, and a mean 2.6, 5, and 9 metastatic nodes were identified, respectively (p < 0.001), a finding that is proportional to the cancer stage (tau-b = 0.284, p < 0.001; rho = 0.3, p < 0.001). The 5-year disease-free survival rate did not differ among the three groups; however, the LNM3 group had the poorest overall and cancer-specific survival rates. Risk factors associated with cancer-specific survival rate were identified with neural invasion, poorly differentiated tumors, and the location of pathologic lymph nodes (LNM). CONCLUSION: Metastatic lymph node location affects oncologic outcomes of stage III colon cancer. The patients for LNM3 metastasis should receive a more aggressive adjuvant treatment.

Lymph node location of a clear cell hidradenoma: report of a patient and review of literature.

Cutaneous clear cell hidradenoma is an uncommon benign adnexal tumor which is not supposed to metastasize, contrary to its rare malignant counterpart, hidradenocarcinoma. We report the case of a 49-year-old man, who had had a stable inguinal lymph node enlargement for 6 years. An excision was performed and revealed an intra-nodal tumor, made of large clear cells with abundant cytoplasm and round nuclei without atypia or mitosis. The immunohistochemical staining showed diffuse positivity for keratin AE1/AE3, keratin 5/6 and p63, and focal staining with keratin 7, epithelial membrane antigen (EMA) and carcinous epithelial antigen (CEA), which underlined some ductular structures. Tumor cells were negative for renal markers PAX8 and CD10. Ki67 stained less than 1% of tumor cells. A translocation involving MAML2 gene was evidenced by fluorescence in situ hybridization (FISH) analysis. No primary cutaneous tumor was found after extensive examination. Altogether, these results are in favor of an isolated nodal hidradenoma, for which we discuss two hypothesis: a primary nodal lesion, or a 'benign metastasis' of a cutaneous tumor. Cases of morphologically benign hidradenoma with lymph node involvement are exceptional. Our case, similar to every other reported case, was associated with an excellent prognosis, supporting the idea that these patients should not be overtreated.