Digital image processing technology applied in level measurement and control system

AbstractAs the diversity of industrial processes, the common level meter devices are more impacted by external factors. This paper presents a new type of digital image processing technology for the level control system, combining with CCD camera technology as one of the measurement method. The fixed beam for measuring needs generated by the laser measurements, shape a special light point on the object surface, We can measure according to the changing scope of these points, or moving distance. From the experiment we can see, the CCD-based level measurement method not only has strong anti-interference ability, good usability, easy adaptability, but also applies to variety of more complex industrial applications

Monitoring the progression of metastatic breast cancer on nanoporous silica chips

Breast cancer accounted for 15 per cent of total cancer deaths in female patients in 2010. Although significant progress has been made in treating early-stage breast cancer patients, there is still no effective therapy targeting late-stage metastatic breast cancers except for the conventional chemotherapy interventions. Until effective therapy for later-stage cancers emerges, the identification of biomarkers for the early detection of tumour metastasis continues to hold the key to successful management of breast cancer therapy. Our study concentrated on the low molecular weight (LMW) region of the serum protein and the information it contains for identifying biomarkers that could reflect the ongoing physiological state of all tissues. Owing to technical difficulties in harvesting LMW species, studying these proteins/peptides has been challenging until now. In our study, we have recently developed nanoporous chip-based technologies to separate small proteins/peptides from the large proteins in serum. We used nanoporous silica chips, with a highly periodic nanostructure and uniform pore size distribution, to isolate LMW proteins and peptides from the serum of nude mice with MDA-MB-231 human breast cancer lung metastasis. By matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and biostatistical analysis, we were able to identify protein signatures unique to different stages of cancer development. The approach and results reported in this study possess a significant potential for the discovery of proteomic biomarkers that may significantly enhance personalized medicine targeted at metastatic breast cancer

Wireless Sensor Network (WSN) Model Targeting Energy Efficient Wireless Sensor Networks Node Coverage

With the rise of the Internet of Things, the application fields of wireless sensor networks (WSN) continue to expand. From agriculture to urban infrastructure monitoring, application requirements in various fields are increasing. The research focuses on designing and improving energy-efficient coverage methods for wireless sensor network nodes, with the goal of improving energy efficiency and data transmission reliability. Through detailed research and analysis of hierarchical and flat routing protocols, the article explores how to ensure that each monitoring point is covered by at least one sensor node by designing an energy-saving sensor network node coverage model. At the same time, the study explores an energy-efficient coverage method based on the improved gray wolf algorithm, aiming to optimize the deployment of sensor nodes and enhance the effectiveness of node coverage. Research results show that the algorithm performs significantly in network coverage optimization and achieves 100% coverage of monitoring target points. Under the 30-dimensional condition, the improved gray wolf algorithm shows excellent average performance and the smallest standard deviation. When the number of nodes is 40, compared with other algorithms, the improved gray wolf algorithm improves the coverage rate by 5.08% and achieves 100% coverage performance in a more energy-saving manner. Research on the exploration of energy-saving wireless sensor network models will help to better meet the needs of future intelligent monitoring and control, improve resource utilization efficiency, reduce maintenance costs, and promote the sustainable development of wireless sensor networks

Application of Chaotic Cat Swarm Optimization in Cloud Computing Multi Objective Task Scheduling

In cloud computing multi-task scheduling, a large number of tasks require the system to have excellent task scheduling capabilities to ensure stable and efficient operation of the system. However, the integration and scheduling of multi-task resources in cloud computing directly affect the effectiveness of cloud computing services. Traditional cloud computing task scheduling has low efficiency and single objective optimization, which cannot meet the requirements of cloud computing tasks. In this regard, cat swarm optimization model is used to construct a multi-objective task scheduling model for cloud computing, achieving cloud computing tasks optimization. Cloud computing task objectives were analyzed, and a multi-objective task scheduling model was constructed with execution time and system load as scheduling objectives. Considering multi-objective task scheduling complexity, cat swarm optimization model was introduced for solution. Cat swarm optimization model can easily fall into local optimization. This model was improved by adjusting weight factor and fitness. In algorithm model performance analysis, Ackley function was used for optimization testing. The new model tends to converge after 445 iterations. At this moment, its optimal optimization value is 0.506, which is superior to other two optimization models. In cloud computing instances analysis, the cloud computing execution time was tested. This proposed model tends to converge after 102 iterations, with a task execution time of 6.23 seconds. The traditional optimization model has a task execution time of 6.51 seconds. The particle swarm optimization model has a task execution time of 6.96 seconds. In task cost analysis, this proposed model has a task execution cost of 3326 yuan when tasks number is 500, which is lower than other two models. From this, it can be seen that the proposed multi-objective task scheduling model has excellent application effects, which can effectively optimize and improve traditional cloud computing multi-objective scheduling, and ensure the stability of system operation. The research content provides important technical support for the management and scheduling optimization of cloud computing resources

Preliminary Structural Characterization of Selenium Nanoparticle Composites Modified by <i>Astragalus</i> Polysaccharide and the Cytotoxicity Mechanism on Liver Cancer Cells

Astragalus alcohol soluble polysaccharide (AASP) could present superior water solubility and antitumor activity with high concentration. Selenium nanoparticles (SeNPs) have received growing attention in various fields, but their unstable property increases the application difficulties. In the present study, functionalized nano-composites (AASP−SeNPs) were synthesized through SeNPs using AASP (average molecular weight of 2.1 × 103 Da) as a surface modifier, and the preliminary structural characteristics and inhibitory mechanism on liver cancer (HepG2) cells were investigated. Results showed that AASP−SeNPs prepared under a sodium selenite/AASP mass ratio of 1/20 (w/w) were uniformly spherical with a mean grain size of 49.80 nm and exhibited superior dispersivity and stability in water solution. Moreover, the composites could dose-dependently inhibit HepG2 cell proliferation and induce apoptosis through effectively regulating mitochondria-relevant indicators including ΔΨm depletion stimulation, intracellular ROS accumulation, Bax/Bcl-2 ratio improvement, and Cytochrome c liberation promotion. These results provide scientific references for future applications in functional food and drug industries

Precursor Chemistry Enables the Surface Ligand Control of PbS Quantum Dots for Efficient Photovoltaics

Abstract The surface ligand environment plays a dominant role in determining the physicochemical, optical, and electronic properties of colloidal quantum dots (CQDs). Specifically, the ligand‐related electronic traps are the main reason for the carrier nonradiative recombination and the energetic losses in colloidal quantum dot solar cells (CQDSCs), which are usually solved with numerous advanced ligand exchange reactions. However, the synthesis process, as the essential initial step to control the surface ligand environment of CQDs, has lagged behind these post‐synthesis ligand exchange reactions. The current PbS CQDs synthesis tactic generally uses lead oxide (PbO) as lead precursor, and thus suffers from the water byproducts issue increasing the surface‐hydroxyl ligands and aggravating trap‐induced recombination in the PbS CQDSCs. Herein, an organic‐Pb precursor, lead (II) acetylacetonate (Pb(acac)2), is used instead of a PbO precursor to avoid the adverse impact of water byproducts. Consequently, the Pb(acac)2 precursor successfully optimizes the surface ligands of PbS CQDs by reducing the hydroxyl ligands and increasing the iodine ligands with trap‐passivation ability. Finally, the Pb(acac)2‐based CQDSCs possess remarkably reduced trap states and suppressed nonradiative recombination, generating a certified record Voc of 0.652 V and a champion power conversion efficiency (PCE) of 11.48% with long‐term stability in planar heterojunction‐structure CQDSCs