An SMDP-based Resource Management Scheme for Distributed Cloud Systems

In this paper, the resource management problem in geographically distributed cloud systems is considered. The Follow Me Cloud concept which enables service migration across federated data centers (DCs) is adopted. Therefore, there are two types of service requests to the DC, i.e., new requests (NRs) initiated in the local service area and migration requests (MRs) generated when mobile users move across service areas. A novel resource management scheme is proposed to help the resource manager decide whether to accept the service requests (NRs or MRs) or not and determine how much resources should be allocated to each service (if accepted). The optimization objective is to maximize the average system reward and keep the rejection probability of service requests under a certain threshold. Numerical results indicate that the proposed scheme can significantly improve the overall system utility as well as the user experience compared with other resource management schemes.Comment: 5 pages, 5 figures, conferenc

Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases

Discovery of novel 5-fluoro-N 2 ,N 4 -diphenylpyrimidine-2,4-diamines as potent inhibitors against CDK2 and CDK9

Novel 5-fluoro-pyrimidine derivatives have been designed, synthesized and evaluated as potential CDK inhibitors as well as antitumor and anti-HIV agents

Clinicopathological characteristics and treatment outcome of resectable gastric cancer patients with small para-aortic lymph node

BackgroundResectable gastric cancer (GC) patients with small para-aortic lymph node (smaller than 10mm in diameter, sPAN) were seldom reported, and existing guidelines did not provide definite treatment recommendation for them.MethodsA total of 667 consecutive resectable GC patients were enrolled. 98 patients were in the sPAN group, and 569 patients without enlarged para-aortic lymph node were in the nPAN group. Standard D2 lymphadenectomy was performed. Neoadjuvant and adjuvant chemotherapy were administrated according to the cTNM and pTNM stage, respectively. Clinicopathological features and prognosis were compared between these two groups.ResultsThe median size of sPAN was 6 (range, 2−9) mm and the distribution was prevalent in No. 16b1. cN stage (p=0.001) was significantly related to the presence of sPAN. sPAN was both independent risk factor for OS (p=0.031) and RFS (p=0.046) of all patients. The prognosis of patients with sPAN was significantly worse than that of patients with nPAN (OS: p=0.008; RFS: p=0.007). Preoperative CEA and CA19-9 were independent risk factors for prognosis of patients with sPAN. Furthermore, patients in the sPAN group with normal CEA and CA19-9 exhibited acceptable prognosis (5-year OS: 67%; RFS: 64%), while those with elevated CEA or CA19-9 suffered significantly poorer prognosis (5-year OS: 17%; RFS: 17%) than patients in the nPAN group (5-year OS: 64%; RFS 62%) (both p < 0.05).ConclusionsStandard D2 lymphadenectomy should be considered a valid approach for GC patients with sPAN associate to normal preoperative CEA and CA19-9 levels. Patients with sPAN associated to elevated CEA or CA19-9 levels could benefit from a multimodal approach: neoadjuvant chemotherapy; radical surgery with D2 plus lymph nodal dissection extended to No. 16 station

A Novel Execution Mode Selection Scheme for Wireless Computing

Computation offloading is an effective way to alleviate the resource limited problem of mobile devices. However, the offloading is not an always advantageous strategy for under some circumstances the overhead in time and energy may turn out to be greater than the offloading savings. Therefore, an offloading decision scheme is in demand for mobile devices to decide whether to offload a computation task to the server or to execute it in a local processor. In this paper, the offloading decision problem is translated into a dynamic execution mode selection problem, the objective of which is to minimize the task execution delay and reduce the energy consumption of mobile devices. A novel execution mode adjustment mechanism is introduced to make the execution process more flexible for real-time environment variation. Numerical results indicate that the proposed scheme can significantly reduce the task execution delay in an energy-efficient way

State-of-the-Art on Technological Developments and Adaptability of Prefabricated Industrial Steel Buildings

Compared to traditional onsite steel construction, prefabricated industrial steel construction (PFISC) saves time, money, and resources. It results in sustainable steel structures that use fewer resources and are better for the environment. Despite their advantages, the private sector favors creating high-rise buildings in an old-fashioned way. In order to encourage the adaptability of prefabricated industrial steel buildings (PFISBs) in high-rise structures, this study critically evaluates the adaptable solutions offered in the literature on the recent developments, structural performances, present difficulties, and future potential. In mid-rise and low-rise structures, PFISC is frequently used. In research and case studies, PFISBs have proven to perform admirably under various adverse conditions, including in the event of an earthquake, wind, blast, impact, fire, collapse, and long-term sustained loads. The use of potential research solutions, the “Top-down” strategy, and the resolving of problems such as the structural-based design guidelines, column stability, discontinuous vertical and horizontal diaphragms, cluster columns and beams effect, damage-free and innovative inter- and intra-modular connections, high strength-to-weight modules, numerical simulation, and transportation will help PFISBs to become more widely accepted in high-rise structures. Compared to other materials, steel has recently demonstrated great promise for the construction of PFISBs. Additionally, China plans to increase their PFISC to 30% by 2026, Australia to 15% by 2025, and North America to over 5% by 2023, proving that it is a reasonable response to future urbanization concerns