Energy Efficient Wireless Sensor Network Modelling Based on Complex Networks

The power consumption and energy efficiency of wireless sensor network are the significant problems in Internet of Things network. In this paper, we consider the network topology optimization based on complex network theory to solve the energy efficiency problem of WSN. We propose the energy efficient model of WSN according to the basic principle of small world from complex networks. Small world network has clustering features that are similar to that of the rules of the network but also has similarity to random networks of small average path length. It can be utilized to optimize the energy efficiency of the whole network. Optimal number of multiple sink nodes of the WSN topology is proposed for optimizing energy efficiency. Then, the hierarchical clustering analysis is applied to implement this clustering of the sensor nodes and pick up the sink nodes from the sensor nodes as the clustering head. Meanwhile, the update method is proposed to determine the sink node when the death of certain sink node happened which can cause the paralysis of network. Simulation results verify the energy efficiency of the proposed model and validate the updating of the sink nodes to ensure the normal operation of the WSN

Physicochemical properties of peanut oil body nutritional emulsions: effects of energy density and nutrient ratio

The physical and chemical stability of peanut oil body (POB) nutritional emulsions and traditional emulsifier-based nutritional emulsions were compared under varying energy densities and nutrient ratios, with a focus on protein adsorption at the oil-water interface. The results demonstrated that flocculation was the primary instability mechanism for POB emulsions, whereas emulsifier-based emulsions predominantly experienced coalescence. At energy densities of 1.9 and 2.1 kcal/mL, POB nutritional emulsions exhibited lower PDI values (0.136 and 0.139), compared to emulsifier-based emulsions (0.152 and 0.191). Additionally, micromorphology analysis indicated enhanced anti-coalescence properties for POB emulsions. The interfacial protein adsorption capacity of POB emulsions (6.8 and 7.0 mg/m2) was also lower than that of emulsifier-based emulsions (7.5 and 8.0 mg/m2), suggesting that the thinner interfacial protein film may contribute to the improved storage stability of POB emulsions. At an energy density of 2.1 kcal/mL, after adjusting the nutrient ratio, the CI values of POB emulsions (8.79%, 3.95%, 3.75%) were consistently lower than those of emulsifier-based emulsions (10.25%, 8.16%, 8.02%), further indicating superior storage stability. Both emulsions showed similar appearance colors. These findings demonstrate that POB emulsions offer a promising alternative to refined oil in nutritional emulsion formulations, effectively replacing traditional emulsifiers, particularly in high-energy-density applications

Utilizing Distributed Heterogeneous Computing with PanDA in ATLAS

In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. The spectrum of computing options keeps increasing across the Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance computing, commercial clouds, and emerging service levels like Platform-as-a-Service (PaaS), Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS), each one providing new advantages and constraints. Users can significantly benefit from these providers, but at the same time, it is cumbersome to deal with multiple providers, even in a single analysis workflow with fine-grained requirements coming from their applications’ nature and characteristics. In this paper, we will first highlight issues in geographically-distributed heterogeneous computing, such as the insulation of users from the complexities of dealing with remote providers, smart workload routing, complex resource provisioning, seamless execution of advanced workflows, workflow description, pseudointeractive analysis, and integration of PaaS, CaaS, and FaaS providers. We will also outline solutions developed in ATLAS with the Production and Distributed Analysis (PanDA) system and future challenges for LHC Run4

Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS

Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. As the size of the dataset increases at the Large Hadron Collider (LHC), and at the same time the search spaces become bigger and bigger in order to exploit the physics potentials, more and more computing resources are required for processing these ML tasks. In addition, complex advanced ML workflows are developed in which one task may depend on the results of previous tasks. How to make use of vast distributed CPUs/GPUs in WLCG for these big complex ML tasks has become a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysis (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describe how PanDA and iDDS deal with large-scale ML workflows, including the implementation to process workloads on diverse and geographically distributed computing resources. Next, we report real-world use cases, such as HyperParameter Optimization, Monte Carlo Toy confidence limits calculation, and Active Learning. Finally, we conclude with future plans

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects

A Novel Thermal Deformation Self-Stabilization Flexible Connection Mechanism

In micro-LED chip repair, a nanopositioner is commonly used to adjust the positioning of the LED chip. However, during the bonding process, the heat generated can cause the positioning system to deform, leading to inaccurate alignment and poor-quality chip repair. To solve this issue, a novel flexible connection structure has been proposed that can eliminate thermal deformation. The principle of this novel flexible connection structure is that the thermal distortion self-elimination performance is achieved via three flexible connection modules (FCM) so that the thermal stress is automatically eliminated. First, the paper introduces the principle of thermal deformation elimination, and then the design and modeling process of the proposed structure are described. A heat transfer model is then developed to determine how temperature is distributed within the structure. A thermal deformation model is established, and the size of the FCM is optimized using a genetic algorithm (GA) to minimize the thermal deformation. Finite element analysis (FEA) is used to simulate and evaluate the thermal distortion self-elimination performance of the optimized mechanism. Finally, experiments are conducted to verify the reliability and accuracy of the simulation results. The simulations and experiments show that the proposed structure can eliminate more than 38% of the thermal deformation, indicating an excellent thermal deformation self-eliminating capability

Effects of aerosolized inhalation of N-acetylcysteine combined with high flow nasal cannulae oxygen therapy on inflammation, oxidative stress, and cognitive function in patients with severe pulmonary infection complicated with respiratory failure

Purpose: To investigate the effects of aerosolized inhalation of N-acetylcysteine (NAC) combined with high-flow nasal cannulae oxygen therapy on serum inflammation, oxidative stress, and cognitive function in patients with severe pulmonary infection complicated with respiratory failure.&#x0D; Methods: A total of 50 patients diagnosed with severe pulmonary infection complicated with respiratory failure were randomly divided into the study and control groups, respectively. Serum oxidative stress, inflammation, and cognitive function were determined after treatment intervention. The effect of aerosol inhalation of NAC combined with high flow-nasal cannulae oxygen therapy was assessed.&#x0D; Results: General clinical data such as age, lung function, etc, did not show any difference between the study and control groups (p &gt; 0.05). Dyspnea was scored in the Study group after treatment. Serum malondialdehyde (MDA), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) decreased (p &lt; 0.05), while cognitive scores and serum total antioxidant capacity of the Study group increased (p &lt; 0.05); There was no significant difference in serum superoxide dismutase levels between the two groups (p &gt; 0.05).&#x0D; Conclusion: Aerosolized inhalation of NAC combined with high-flow nasal cannulae oxygen therapy has anti-inflammatory and antioxidant effects, which can improve cognitive dysfunction in patients with severe pulmonary infection complicated with respiratory failure, and may provide a new clinical treatment strategy for severe pulmonary infection complicated with respiratory failure.</jats:p