On the extremal number of edges in hamiltonian connected graphs

AbstractAssume that n and δ are positive integers with 3≤δ<n. Let hc(n,δ) be the minimum number of edges required to guarantee an n-vertex graph G with minimum degree δ(G)≥δ to be hamiltonian connected. Any n-vertex graph G with δ(G)≥δ is hamiltonian connected if |E(G)|≥hc(n,δ). We prove that hc(n,δ)=C(n−δ+1,2)+δ2−δ+1 if δ≤⌊n+3×(nmod2)6⌋+1, hc(n,δ)=C(n−⌊n2⌋+1,2)+⌊n2⌋2−⌊n2⌋+1 if ⌊n+3×(nmod2)6⌋+1<δ≤⌊n2⌋, and hc(n,δ)=⌈nδ2⌉ if δ>⌊n2⌋

Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis

Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability. While machine learning (ML) models are used for DoS attack detection, new strategies are needed to enhance their performance. We suggest an innovative method, combinatorial fusion, which combines multiple ML models using advanced algorithms. This includes score and rank combinations, weighted techniques, and diversity strength of scoring systems. Through rigorous evaluations, we demonstrate the effectiveness of this fusion approach, considering metrics like precision, recall, and F1-score. We address the challenge of low-profiled attack classification by fusing models to create a comprehensive solution. Our findings emphasize the potential of this approach to improve DoS attack detection and contribute to stronger defense mechanisms.Comment: 6 pages, 3 figures, IEEE CN

Sensor feature selection and combination for stress identification using combinatorial fusion

The identification of stressfulness under certain driving condition is an important issue for safety, security and health. Sensors and systems have been placed or implemented as wearable devices for drivers. Features are extracted from the data collected and combined to predict symptoms. The challenge is to select the feature set most relevant for stress. In this paper, we propose a feature selection method based on the performance and the diversity between two features. The feature sets selected are then combined using a combinatorial fusion. We also compare our results with other combination methods such as naive Bayes, support vector machine, C4.5, linear discriminant function (LDF), and k-nearest neighbour (kNN). Our experimental results demonstrate that combinatorial fusion is an efficient approach for feature selection and feature combination. It can also improve the stress recognition rate.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000322766600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701RoboticsSCI(E)EI1ARTICLEnull1

A Retrospective Interventional Cohort Study to Assess the Safety and Efficacy of Sandostatin LAR for Treatment of Recurrent and/or Refractory Meningiomas.

Background: Meningiomas are the most common adult primary intracranial tumors in the United States. Despite high recurrence rate of atypical and malignant subtypes, there is no approved drug indicated specifically for meningioma. Since the majority of meningiomas exhibit high density of somatostatin receptors subtypes, somatostatin analogs have been under close investigation. The aim of this study was to evaluate efficacy and safety of Sandostatin LAR (octreotide) in patients with progressive, and/or recurrent meningioma, and identify subset of patients who were more likely to benefit from this treatment. Methods: A total of 43 patients ≥ 18 years old were included in the retrospective chart review. The patients underwent treatment with Sandostatin LAR (octreotide) from 01.01.2010 to 06.01.2017 at the University of California, Irvine after confirmation of the diagnosis. Six months progression free survival (PFS6) was defined as a primary endpoint, and the overall survival (OS), safety, and toxicity were identified as secondary endpoints. Results: The OS for 6 months, 1, and 3 years for all WHO grades was 94.8, 88.1, and 67.0%, respectively. The PFS6 for WHO I, II, III, and all was 89.4, 89, 33.3, and 80% respectively. For patients with no prior surgeries, chemotherapy or radiation, the PFS6 was 88.9, 84.8, and 94.8%, respectively. Interestingly, the PFS6 was 90.5% for skull-based and 80% for 3-6 cm tumors. Patients with tumors in parasagittal location had PFS6 of 83.3% compared to PFS6 of 50.0% for patients with convexity tumors. Evaluation of PFS6 based on the effect of estrogen and progesterone on meningioma identified that ER-PR+ tumors had PFS6 of 87.8% while patients with ER-PR- meningiomas had PFS6 of 62.5%. Median TTP for WHO grade I, II, and III was 3.1, 2.40, and 0.26 years, respectively. Subgroup analysis showed that median TTP was 3.1 years for &lt;3 cm tumors, 3.22 years for skull-based tumors, 2.37 years for patients with prior surgeries and 3.10 years for patients with no history of chemotherapy. History of radiation had no effect on median TTP. Sandostatin LAR (octreotide) was well-tolerated. Conclusions:This is one of the largest retrospective analysis of meningioma patients treated with Sandostatin LAR (octreotide) suggesting that this treatment has minimal to no adverse events and could prolong overall survival, and progression free survival especially for patients with ER-PR+ tumors who underwent surgeries for small skull-based tumors

Magnetic Tower Outflows from a Radial Wire Array Z-pinch

We present the first results of high energy density laboratory astrophysics experiments which explore the evolution of collimated outflows and jets driven by a toroidal magnetic field. The experiments are scalable to astrophysical flows in that critical dimensionless numbers such as the Mach number, the plasma beta and the magnetic Reynolds number are all in the astrophysically appropriate ranges. Our experiments use the MAGPIE pulsed power machine and allow us to explore the role of magnetic pressure in creating and collimating the outflow as well as showing the creation of a central jet within the broader outflow cavity. We show that currents flow along this jet and we observe its collimation to be enhanced by the additional hoop stresses associated with the generated toroidal field. Although at later times the jet column is observed to go unstable, the jet retains its collimation. We also present simulations of the magnetic jet evolution using our two-dimensional resistive magneto-hydrodynamic (MHD) laboratory code. We conclude with a discussion of the astrophysical relevance of the experiments and of the stability properties of the jet.Comment: Accepted by MNRAS. 17 pages without figures. Full version with figures can be found at http://www.pas.rochester.edu/~afrank/labastro/MF230rv.pd

Western Pacific atmospheric nutrient deposition fluxes, their impact on surface ocean productivity

The atmospheric deposition of both macronutrients and micronutrients plays an important role in driving primary productivity, particularly in the low-latitude ocean. We report aerosol major ion measurements for five ship-based sampling campaigns in the western Pacific from similar to 25 degrees N to 20 degrees S and compare the results with those from Atlantic meridional transects (similar to 50 degrees N to 50 degrees S) with aerosols collected and analyzed in the same laboratory, allowing full incomparability. We discuss sources of the main nutrient species (nitrogen (N), phosphorus (P), and iron (Fe)) in the aerosols and their stoichiometry. Striking north-south gradients are evident over both basins with the Northern Hemisphere more impacted by terrestrial dust sources and anthropogenic emissions and the North Atlantic apparently more impacted than the North Pacific. We estimate the atmospheric supply rates of these nutrients and the potential impact of the atmospheric deposition on the tropical western Pacific. Our results suggest that the atmospheric deposition is P deficient relative to the needs of the resident phytoplankton. These findings suggest that atmospheric supply of N, Fe, and P increases primary productivity utilizing some of the residual excess phosphorus (P*) in the surface waters to compensate for aerosol P deficiency. Regional primary productivity is further enhanced via the stimulation of nitrogen fixation fuelled by the residual atmospheric iron and P*. Our stoichiometric calculations reveal that a P* of 0.1 mu mol L-1 can offset the P deficiency in atmospheric supply for many months. This study suggests that atmospheric deposition may sustain similar to 10% of primary production in both the western tropical Pacific

DependData: data collection dependability through three-layer decision-making in BSNs for healthcare monitoring

Recently, there have been extensive studies on applying security and privacy protocols in Body Sensor Networks (BSNs) for patient healthcare monitoring (BSN-Health). Though these protocols provide adequate security to data packets, the collected data may still be compromised at the time of acquisition and before aggregation/storage in the severely resource-constrained BSNs. This leads to data collection frameworks being meaningless or undependable, i.e., an undependable BSN-Health. We study data dependability concerns in the BSN-Health and propose a data dependability verification framework named DependData with the objective of verifying data dependability through the decision-making in three layers. The 1st decision-making (1-DM) layer verifies signal-level data at each health sensor of the BSN locally to guarantee that collected signals ready for processing and transmission are dependable so that undependable processing and transmission in the BSN can be avoided. The 2nd decision-making (2-DM) layer verifies data before aggregation at each local aggregator (like clusterhead) of the BSN to guarantee that data received for aggregation is dependable so that undependable data aggregation can be avoided. The 3rd decision-making (3-DM) layer verifies the stored data before the data appears to a remote healthcare data user to guarantee that data available to the owner end (such as smartphone) is dependable so that undependable information viewing can be avoided. Finally, we evaluate the performance of DependData through simulations regarding 1-DM, 2-DM, and 3-DM and show that up to 92% of data dependability concerns can be detected in the three layers. To the best of our knowledge, DependData would be the first framework to address data dependability aside from current substantial studies of security and privacy protocols. We believe the three layers decision-making framework would attract a wide range of applications in the future