Problem Of Distribution Research Articles

Fine-grained concrete properties in depending on carbon black additive introducing methods

The object of research is fine-grained concrete with carbon black additive obtained from the recycling of rubber tires. This study aims to determine a method for introducing carbon black into fine-grained concrete that achieves the best strength characteristics. The first method involves the preliminary preparation of a suspension by mixing a superplasticizer, carbon black, and part of the water. The second is the granulation of carbon black together with a superplasticizer in the ratio water: superplasticizer = 9: 1, followed by its introduction into the dry mixture and the addition of water at the last stage of mixing the raw material mixture. The addition of carbon black in powder form to the concrete mixture resulted in a decrease in strength from 35.1 MPa (without carbon black) to 34.9–25.6 MPa (with 4–12% carbon black). The effectiveness of the method of introducing granular carbon black was established, with an increase in strength by 40% in relation to the mixture obtained by the first method. The best strength of 50 MPa was demonstrated by the mix with 4% granular carbon black, which was 6% higher than that of the reference mix. Granulation of carbon black makes it possible to reduce its hydrophobicity and solve the problem of uniform distribution of carbon particles during homogenization of the raw material mixture in the presence of water.

Synthesis of sparse rectangular planar arrays with weight function and improved grey wolf optimization algorithm

AbstractThe present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

A virtual-reality spatial matching algorithm and its application on equipment maintenance support: System design and user study

Equipment maintenance support is an important technical measure to maintain the equipment’s expected performance. However, the current maintenance supports are mainly completed by maintainers under the guidance of technical manual or additional experts, which may be insufficient for some advanced equipment with rapid update rate and complex inner structure. The rising technology of augmented reality (AR) provides a new solution for equipment maintenance support, while one of the key issues limiting the practical application of AR in maintenance field is the spatial matching issue between virtual space and reality space. In this paper, a virtual-reality spatial matching algorithm is designed to accurately superimpose the virtual information to the corresponding actual scene on the AR glasses. In this algorithm, two methods are proposed to help achieve the stable matching of virtual space and reality space. In detail, to obtain the saliency map with less background interference and improved saliency detection accuracy, a saliency detection method is designed based on the super-pixel segmentation. To deal with the problems of uneven distribution on the feature points and weak robustness to the light changes, a feature extraction and matching method is proposed for acquiring the feature point matching set with the utilization of the obtained saliency map. Finally, an immersive equipment maintenance support system (IEMSS) is developed based on this spatial matching algorithm, which provides the maintainers with immediate and immersive guidance to improve the efficiency and safety in the maintenance task, as well as offers maintenance training for inexperienced maintainers with expanded virtual information in case of limited experts. Several comparative experiments are implemented to verify the effectiveness of proposed methods, and a user study of real system application is carried out to further evaluate the superiority of these methods when applied in the IEMSS.

Investigating of Carbon Agent Distribution Using Long-Chain Alkyl in Grafted Binder for High-Capacity SiOx/Graphite Anode

A small amount of the conductive carbon agent in lithium-ion battery (LIB) formulation has a significantly impact on the rate capability performance of battery but the distribution problem of the carbon conductive material is issued in the electrode slurry due to its hydrophobic and inert characteristics. To overcome this problem, the use of dispersant agent was proposed in the electrode fabrication process.In this study, the grafting of adhesive acrylamide monomer (AM) and a long-chain alkyl monomer onto carboxymethyl cellulose (CMC) is applied to binder to improve the conductive agent distribution in a high-capacity SiOx/C anode. The long alkyl chain in the monomer of the graft copolymer binder exhibits good dispersibility of the carbon agent and improved conductive matrix compared to the binder with no alkyl monomer. This may be explained by CH-π integration between the alkyl monomer in graft polymer and carbon agent. The high-capacity SiOx/C anode with the long-alkyl chain introduced graft binder is examined in the slurry level with rheology and dispersion stability, in the electrode sheet level with electric resistance, peel-off, and coin-cell level with various electrochemical performance, charge/discharge performance. Keywords: Silicon oxide; Graphite; Carbon agent distribution, long-chain alkyl monomer; Acrylamide; Graft copolymer Figure 1

Processing correlations of laser direct energy deposited Inconel718 based on multi-field numerical simulation

Laser direct energy deposited (LDED) Inconel718 always have problems of non-uniform temperature distribution because of the complex flow of molten pool and layer-by-layer fabrication characterization which directly affected the microstructure and mechanical properties of materials. In this work, Inconel718 revealed a refined grains and a weave structure exhibiting excellent mechanical properties which were superior to those of the conventional casting process were successfully fabricated by adopt a high-precision and intelligent coaxial powder feeding system during the LDED processing. Moreover, based on multi-field numerical simulation method, a two-phase flow model and a transient thermal mechanical deformation model were established to simulate the LDED processing, while takes into account the evaporated mass loss of the inconel718 alloy. The morphology prediction of laser directed energy deposition by multi-physics numerical simulation, the relationship among the laser process parameters, i.e., the laser power, scanning speed and the amount of powder feeding, the temperature filed and the microstructure and mechanical properties of Inconel718 alloy during LDED were investigated and established. The simulated results consistent well with the experimental results. Results shown that the relative density of sample was sensitive to the laser power, the precision and stability of the powder feeding rate. With increasing laser power, the grain size of Inconel718 is refined, and the microhardness and tensile properties of the materials increased. The optimized LDED processing parameters were determined. Evaluating the mechanical properties of the materials LDEDed at a laser power of 600W and a scanning speed of 8.5mms-1 exhibited a high tensile strength of 944±5 MPa and a good elongation of 29 ± 0.5 %. This work suggests that the processing parameters can be optimized to prepare densified materials with excellent mechanical properties.

A multi-objective brain storm optimization for integrated distributed flexible job shop and distribution problems

Production and distribution are critical components of the furniture supply chain, and achieving optimal performance through their integration has become a vital focus for both the academic and business communities. Moreover, as economic globalization progresses, distributed manufacturing has become a pioneering production technique. Via leveraging a distributed flexible manufacturing system, mass flexible production at lower costs can be achieved. To this end, this study presents an integrated distributed flexible job shop and distribution problem to minimize makespan and total tardiness. In our research, a set of custom furniture orders from different customers are processed among flexible job shops and then delivered by vehicles to customers as the due date. To distinctly show the presented problem, a mixed integer mathematical programming model is created, and a multi-objective brain storm optimization method is introduced considering the problem's features. In comparison to the other three advanced methods, the superiority of the algorithm created is showcased. The findings of the experiments demonstrate that the constructed model and the introduced algorithm have remarkable competitiveness in addressing the problem being examined.

Sedimentary build-ups of pre-salt isolated carbonate platforms and formation of deep-water giant oil fields in Santos Basin, Brazil

In response to the problems of unclear distribution of deep-water pre-salt carbonate reservoirs and formation conditions of large oil fields in the Santos passive continental margin basin, based on comprehensive utilization of geological, seismic, and core data, and reconstruction of Early Cretaceous prototype basin and lithofacies paleogeography, it is proposed for the first time that the construction of pre-salt carbonate build-ups was controlled by two types of isolated platforms: inter-depression fault-uplift and intra-depression fault-high. The inter-depression fault-uplift isolated platforms are distributed on the present-day pre-salt uplifted zones between depressions, and are built on half- and fault-horst blocks that were inherited and developed in the early intra-continental and inter-continental rift stages. The late intra-continental rift coquinas of the ITP Formation and the early inter-continental rift microbial limestones of the BVE Formation are continuously constructed; intra-depression fault-high isolated platforms are distributed in the current pre-salt depression zones, built on the uplifted zones formed by volcanic rock build-ups in the early prototype stage of intra-continental rifts, and only the BVE microbial limestones are developed. Both types of limestones formed into mound-shoal bodies, that have the characteristics of large reservoir thickness and good physical properties. Based on the dissection of large pre-salt oil fields discovered in the Santos Basin, it has been found that both types of platforms could form large-scale combined structural-stratigraphic traps, surrounded by high-quality lacustrine and lagoon source rocks at the periphery, and efficiently sealed by thick high-quality evaporite rocks above, forming the optimal combination of source, reservoir and cap in the form of “lower generation, middle storage, and upper cap”, with a high degree of oil and gas enrichment. It has been found that the large oil fields are all bottom water massive oil fields with a unified pressure system, and they are all filled to the spill-point. The future exploration is recommended to focus on the inter-depression fault-uplift isolated platforms in the western uplift zone and the southern section of eastern uplift zones, as well as intra-depression fault-high isolated platforms in the central depression zone. The result not only provides an important basis for the advanced selection of potential play fairways, bidding of new blocks, and deployment of awarded exploration blocks in the Santos Basin, but also provides a reference for the global selection of deep-water exploration blocks in passive continental margin basins.

Hot spot localization in the field of view of the Kirkpatrick–Baez microscope

The Kirkpatrick–Baez (KB) microscope is an effective instrument for x-ray imaging of hot spot. However, the non-uniform distribution of response efficiency in the field of view is a drawback of the KB microscope. A more accurate hot spot image requires the correction of the measured image by combining the hot spot position and the response efficiency distribution. Here, we describe a method to locate the position of the hot spot in the field of view during hot spot imaging with a KB microscope. The position of the hot spot in the field of view can be obtained by measuring the grazing incidence angle change during hot spot imaging. In the experiment of hot spot self-emission imaging with a four-channel KB microscope, the location of the hot spot with an accuracy of 15 μm was realized, and the intensity corrected hot spot image was obtained. This will solve the problem of the non-uniform distribution of the response efficiency of the KB microscope and enable quantitative measurement of hot spot radiation intensity.

Vehicle routing problem for cold-chain drug distribution with epidemic spread situation

We investigate a vehicle routing problem considering the influence of epidemic spread (VRP-ES) for the design of a novel cold-chain drug distribution system, in which the disease spread model is used to capture virus transmission characteristics and demand fluctuations. To this end, we aim to minimize the total travel time and transmission risk of the distribution network by incorporating realistic features including priority distribution and temperature control. We propose a hybrid tabu search heuristic (HTS) with a specifically designed initial solution, several neighborhood operators, and diversification strategies to solve this problem. A series of numerical experiments are conducted to test the proposed solution methodology. The virus spread model and vehicle routing results are discussed to analyze the VRP-ES optimization strategies through an empirical case study of Chongqing city in China. Sensitivity analysis is conducted to identify the impact of various parameters on the VRP-ES and provide some management implications as well.

Central versus deployed drinking water treatment

Nowadays, the level of urbanization is in rapid increasing, but the freshwater resources still have the problems of unbalanced distribution of regions and time. Besides, the freshwater resources are still in short supply. However, in fact, there is always a hidden resource in the city - the wastewater. The rapid population growth and increasing urbanization have increased the needs for wastewater treatment and recycling to ensure water resources adequacy, and then improve the environmental quality. In this day and age, there are two main ways to treat wastewater: centralized treatment and decentralized treatment. Both of these methods can reduce the pollution of wastewater to water resources and provide domestic water. In this article, we will introduce and compare the two approaches with real-world examples: The Shanghai Bailonggang Sewage Treatment Plant, located in Chaoyang Village, Heqing Town, Pudong New Area, and the decentralized sewage treatment system in Germany.

Digital mobile fronthaul scheme based on diff-delta-sigma modulation and OCT precoding.

In this Letter, we experimentally demonstrate digital mobile fronthaul (MFH) for 65536 quadrature amplitude modulation (65536-QAM) signals based on a diff-delta-sigma modulation (D-DSM) scheme with orthogonal circulant matrix transform (OCT) precoding. By combining the D-DSM scheme with OCT precoding, we successfully solved the problem of uneven distribution of in-band quantization noise (IBN) while bringing about a quantization SNR gain of about 1.5 dB. In addition, we compare two signal combination schemes, Gray coding and power superposition, in the D-DSM scheme. The results show that the power superposition scheme can achieve similar performance to Gray coding with lower computational complexity. At the same time, the power superposition scheme is less affected by the saturation effect. Therefore, the D-DSM solution combined with OCT precoding and power superposition provides an effective solution for future 6 G digital mobile fronthaul.

Research on the Balanced Development of Urban and Rural Compulsory Education Through Education Resource Allocation

With the rapid development of our country’s economy, education has gradually become the focus of social attention. The problem of unbalanced distribution of urban and rural educational resources has become increasingly prominent, urban educational resources are relatively rich, while rural educational resources are relatively scarce, and the balanced development of urban and rural compulsory education has become an urgent task. This paper mainly investigates and studies the distribution of urban and rural educational resources, discusses the unbalanced distribution of urban and rural educational resources and analyzes the reasons, and puts forward a series of corresponding solutions to promote the balanced development of urban and rural compulsory education.

A pest image recognition method for long-tail distribution problem

A pest image recognition method for long-tail distribution problem

Proposal of a novel solar-driven thermochemical reactor for green hydrogen production using helical flow channels

The reactor is the core component of the hydrogen production system. Although hydrogen production using solar energy as the heat source can effectively reduce carbon emissions, the reactor faces the problem of uneven heating caused by the collector, and the uneven temperature distribution will significantly affect the hydrogen production process. A key characteristic of the heat collection in dish solar collectors is that the energy flux density is highest at the center and gradually decreases radially, resulting in an uneven heat distribution on the reactor surface. Therefore, the paper focuses on addressing the problem of the uneven distribution of solar concentrating temperature affecting the working efficiency of the reactor. In this study, the reactor’s flow channel is designed in a helical configuration to enable the reaction gas to enter at the edge, gradually flow inward along the helical channel, and exit at the central position. Through simulation studies of the reactor under various working conditions, it was found that the helical structure can effectively facilitate the continuous temperature rise of the reaction gas, with a methane conversion rate of 94.76 %, a hydrogen yield of 1.54 mol/mol, and a total energy conversion efficiency of 32.52 %. Thus, the design of the helical flow channels can effectively utilize the uneven solar heat distribution, allowing the reactor to operate efficiently. Previous studies have focused on enhancing the heat transfer performance of endothermic materials to achieve temperature uniformity; however, this paper adopts a novel approach by innovating the flow channel structure. This paper offers a new concept for the design of solar thermochemical reactors.

Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

Advances in the selection and timing of postoperative radioiodine treatment in patients with differentiated thyroid carcinoma.

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy. Patients who receive systematic care typically have a better prognosis. RAI treatment plays a key role in eradicating any remaining thyroid lesions in DTC patients, hence decreasing the risk of distant metastases and cancer recurrence. As research continues to advance, RAI treatment is becoming more and more individualized. Because of the excellent prognosis for DTC patients, there is a relatively broad window for RAI treatment, making it easy to overlook when to receive RAI treatment. However, research on this issue can help patients with varying recurrence risk stratification make better decisions about when to begin RAI treatment following surgery, and physicians can schedule patients based on the severity of their disease. This will improve patient prognosis and lessen needless anxiety in addition to helping solve the problems of unjust healthcare resource distribution. In this review, we will mainly discuss the target population of RAI treatment as well as studies that examine the impact of RAI treatment timingon patient outcomes. In an effort to discourage DTC patients and physicians from selecting RAI therapy at random, we also review the possible negative effects of this treatment.

ПОЛИХЛОРИРОВАННЫЕ БИФЕНИЛЫ В ВОДНОЙ ЭКОСИСТЕМЕ ЗАПОВЕДНОГО ОЗЕРА МАРКАКОЛЬ И ВПАДАЮЩИХ В НЕГО РЕК

The article presents the results of the study of polychlorinated biphenyls (PCBs) pollution of water and bottom sediments of Markakol Lake and rivers of its basin. Brief information about the importance of studying the accumulation and distribution of these dangerous xenobiotics in water bodies and watercourses of the republic in order to realize the tasks adopted by the Republic of Kazakhstan in accordance with the requirements of the global Stockholm Convention on Persistent Organic Pollutants (POPs) is presented. The conducted research f irst of all showed pollution of water resources and bottom sediments of the lake and rivers located in the protected area. Uneven distribution of toxicant concentration over the lake water area both in water and bottom sediments was revealed. The highest toxicant concentrations were registered in the water of the Kalzhyr River outflowing from the lake. In lake and river waters, 30 individual PCB congeners were detected, among which highly toxic dioxin-like and «marker» congeners are present. Other congeners with rather high toxicity were also found in water resources. Based on the scientific results of scientists from far and near abroad in the field of problems of distribution of these toxicants on the planet, the hypothesis about atmospheric transfer of these toxic compounds to this protected zone from highly PCB polluted territories was made.

Instance type completion in equipment knowledge graph based on translation model

Knowledge graph completion is one of the steps of knowledge graph construction. It aims to complete the incomplete triple data in the initially constructed knowledge graph and make the data in the knowledge graph more abundant and complete. At present, there are few studies on knowledge graph completion in the equipment field, and the attribute and relationship samples in the equipment knowledge graph are prone to uneven distribution, while the traditional knowledge graph completion method is difficult to solve the problem of uneven distribution of attribute and relationship samples. Therefore, this paper proposes an internal instance type completion method of equipment knowledge graph based on EP2TP-TRT. First, the TransE model is used to embed the relationship and attributes of the equipment instance, respectively. Then, the EP2TP model is used to map the instance attributes, and the TRT model is used to map the type relationship. Finally, the scores of the EP2TP and TRT models are integrated by designing different weights, and the training prediction is carried out to enhance the representation ability of instance information and type information. Compared with the mainstream advanced models, this method improves the MRR and HITS @ 1 indicators by about 0.89% and 2.1%, respectively.

Attention mechanism based on deep learning for defect detection of wind turbine blade via multi-scale features

Abstract An enhanced wind turbine blade surface defect detection algorithm, CGIW-YOLOv8, has been introduced to tackle the problems of uneven distribution of defect samples, confusion between defects and background, and variations in target scales that arise during drone maintenance of wind turbine blades. This algorithm is given based on the YOLOv8 model. Initially, a data augmentation method based on geometric changes and Poisson mixing was used to enrich the dataset and address the problem of uneven sample distribution. Subsequently, the incorporation of the Coordinate Attention (CA) mechanism into the Backbone network improved the feature extraction capability in complex backgrounds. In the Neck, the Reparameterized Generalized Feature Pyramid Network (Rep-GFPN) was introduced as a path fusion strategy and multiple cross-scale connections are fused, which effectively enhances the multi-scale expression ability of the network. Finally, the original CIOU loss function was replaced with Inner-WIoU, which was created by applying the Inner-IoU loss function to the Wise-IoU loss function. It improved detection accuracy while simultaneously speeding up the model's rate of convergence. Experimental results show that the mAP of the method for defect detection reaches 92%, which is 5.5% higher than the baseline network. The detection speed is 120.5 FPS, which meets the needs of real-time detection.

Synergistic insights: Exploring continuous learning and explainable AI in handwritten digit recognition

Deep Neural Networks achieve outstanding results; however, their reliance on a static environment with fixed data poses challenges in dynamic scenarios where data continuously evolves. Being capable of learning, adapting, and generalizing continually in a scalable, successful, and efficient manner is crucial for the sustainable development of AI systems. The classical solution of retraining the model using both old and new data is time-consuming and expensive. Continual Learning tackles the problem of learning new data distributions without the need for retraining from scratch. Furthermore, the task of recognizing unlabeled images using previously acquired knowledge becomes challenging, particularly when the new data needs to be incrementally annotated without starting the training process from scratch.To gain a deeper understanding of how “Black Box” neural networks make decisions, it is important to visualize components inside the model that affect the error rate throughout the decision-making process. The Continual Self-Learning model on label-less historical digits yields increasingly perceptive interpretations. This paper aims to establish a literature review of the latest advances in continual learning for computer vision tasks, to articulate catastrophic forgetting using Explainable Artificial Intelligence on both split MNIST and the historical digit dataset DIDA, and to shed light on important but still understudied topics.