Map coloring is a classic problem in graph theory and it relates to many optimization techniques in mathematics such as linear programming and simulated annealing. This paper investigates the minimum number of colors required to color a map under different constraints and situations using linear programming. Specifically, it examines three different scenarios: (1) coloring each district on the map with the constraint that adjacent districts must be colored differently, (2) adding the additional constraint that two regions bordering the same region cannot be colored the same, and (3) assigning two colors to each district with the constraint that adjacent districts must be colored differently. To proceed with the research, hypotheses are formulated regarding the impact of these additional constraints on the minimum number of colors required to color the map. The data in the paper is collected by creating sample maps and analyzing the minimum number of colors required to color them under each of the different scenarios. The findings of this research suggest that the addition of constraints, indicating a complex situation, increases the minimum number of colors needed to color the map. Thus, linear programming is found to be an effective optimization technique for solving map coloring problems under these constraints. This research makes a valuable contribution to the field of mathematics and computer science, providing insights into the application of optimization techniques to real-world problems like map coloring. The findings of the research have significant implications for practitioners working in the field of optimization and inform the development of more efficient algorithms for solving map coloring problems.

Nuclear fusion, a process that has the potential to revolutionize the world's energy landscape, is the subject of extensive research due to its promise of providing a clean and safe energy source. The article outlines the essential principles of nuclear fusion and the chronology of its discovery, from early predictions to its initial realization in the first half of the 20th century. It also highlights the extreme requirements and challenges associated with fusion. Furthermore, the article introduces two natural nuclear fusion reactions: thermonuclear fusion and pycnonuclear fusion. In the final section, the focus shifts to artificial nuclear fusion, discussing the progression from the uncontrollable hydrogen bomb to efforts toward controlled atomic fusion since the mid-20th century. The article emphasizes various nuclear fusion configurations (Tokamak, Stellarator, ICF, Magnetic mirrors, and z-pinch) that have been proposed globally, detailing their features, strengths, and weaknesses.

After constructing the Circular Electron Positron Collider (CEPC) and the High-Luminosity Large Hadron Collider (HL-LHC), particle physics experiments will reach a new mass region with more incredible energy. Both types of colliders have the unique duty of searching for new particles or estimating the coupling constants of the reactions based on their different structures, providing a different focus. This presentation will discuss and cover the regions of CEPC and HL-LHC to show their complementary functions. The CEPC can answer the Higgs particle, whether it is a composite particle, how it contributes to the dark matter mass, and whether its field provides enough matter mass for the universe. It can generally provide detections below 10 TeV, leading to possible new theories. For the HL-LHC, the upgraded HL-LHC has a higher luminosity and data acquisition capability, ten times higher than predicted. It is expected to produce 15 million Higgs particles annually, five times more than the LHC. The large number of collision events provides more opportunities to measure the characteristics of the Higgs particles.

The prevailing technology for pedestrian recognition in unmanned driving, predominantly reliant on LiDAR, confronts the dual challenges of elevated expenses and limited anti-interference capabilities. To surmount these obstacles, this paper introduces an inventive fusion methodology that harmonizes inputs from visual cameras, 4D millimeter wave radar, and thermal imaging sensors. The advantages and promising development prospects of 4D millimeter wave radar over laser radar are comprehensively elucidated. By leveraging advanced signal processing algorithms, a robust mathematical model is formulated, facilitating the synthesis of information from a multitude of distinctive feature parameters. In tandem, an assessment of the hazard index is executed using the analytic hierarchy process, enriching vehicular safety and driving efficiency. This innovative approach strives to foster the progression of autonomous vehicle technology and expedite its commercial assimilation into the burgeoning autonomous driving market. By harnessing the synergistic capabilities of multiple sensor modalities, the proposed fusion technique not only addresses the existing limitations but also charts a transformative course towards a safer and more efficient autonomous driving landscape. Through the amalgamation of these cutting-edge technologies, this research aspires to carve a path for the accelerated evolution and widespread deployment of autonomous vehicles.

Gravitational lensing, since Einstein proposed, has developed into an essential part of astronomical exploration. On this basis, lots of projects will observe high-intensity explosive transients, such as gravitational waves generated by the merging of dense binaries, so as to improve the accuracy of cosmological observation. Strong gravitational lensing is an effective observation method, which can be used to observe dark mass (sub) halos and test various dark matter models. This study takes Gravitational wave as an example, analyses the application background and mode of explosive transient, and introduces the new measurement methods for dark matter detection. The measurement of redshifted chirp mass and luminosity distance of Gravitational wave is introduced through the formula. Analysis of the characteristics of dark of the pertuber through more group detections will be helpful for new investigation method in the future. In order to make a credible judgment of the nature of the universe, more experiments need to be carried out in these two aspects. This study briefly analyses the progress in these two fields, aiming to encourage more future exploration.

This article provides a comprehensive review of the three most popular candidates for dark matter: axions, sterile neutrinos, and WIMPs (Weakly Interacting Massive Particles). The article explores the theoretical origins of these candidates and their characteristic properties. It also examines the various observational constraints placed on them by different experiments, including direct and indirect detection experiments, as well as astrophysical and cosmological observations. The article also discusses the implications of these particle candidates on the development of cosmic structures, such as galaxies and galaxy clusters. This review aims to enhance comprehension of the present status in dark matter studies and the challenges faced in identifying the nature of dark matter. In summary, this comprehensive review provides a comparative analysis of the most promising dark matter candidates, shedding light on the latest developments in this exciting field of research.

Charging pile technology is of great significance for the popularization of electric vehicles and the construction of smart grids. By analyzing the development status and trend of charging pile technology against the background of the smart grid, this paper discusses the future development direction of charging pile technology. The research results show that the development prospect of charging pile technology under the background of smart grids is very broad, and it is necessary for the government and enterprises to make joint efforts to solve the problems of high construction costs and unreasonable layout of charging piles through technological innovation and policy support, promote the rapid development of charging pile technology, and provide better support for the development of the electric vehicle market. Charging pile technology is an important device connecting electric vehicles and power grids, which is of great significance for the popularization of electric vehicles and the construction of smart grids. This essay designs a kind of high-power intelligent charging pile which can meet the security protection level, carry out real-time monitoring, real-time data acquisition and interconnection.

Compartmental disease transmission models are widely used to model state transmission in infectious diseases, using differential equations to model the change in the number of units in different states over time, and recently has produced significant practical implications in many downstream fields. However, inspired by the transmission of rumors in social media, we note that the previous compartmental transfer models neglect the "competitiveness" during the transfer process, that is, the "infection" of people with positive and negative opinions to "susceptibles" or even people with opposing views. To tackle the above issues, in this paper, we propose a novel competitive infectious transmission model in which the "infection" will lead to more people supporting the opinion of the infector, effectively establishing the change of the number of units in the positive, negative, and neutral parties over time. In addition, we performed extensive theoretical analysis to investigate the property of the disease-free equilibrium and to calculate the basic reproduction numbers for three different scenarios. For each system, we derive explicit solutions for the basic reproduction numbers and discuss their important implications for guidance in practice.

Respiratory diseases are one of the leading causes of death around the world and they severely affect patient quality of life. Auscultation is an essential method for diagnosing respiratory diseases, and it is low-cost and convenient. However, auscultation requires experts who are highly experienced. Medical trainees suffer from misdiagnosis inevitably. To address this issue, a novel machine learning model is proposed, which consists of upsampling convolutional neural network (CNN), a long short-term memory network (LSTM), and a fully connected network (FCNN) with embedding layers to classify respiratory sounds into seven categories: Normal (N), Rhonchi (R), Wheeze (W), Stridor (S), Coarse Crackle (CC), Fine Crackle (FC), Wheeze & Crackle (WC). The model is trained and evaluated on the SPRSound dataset and obtained the result on the test dataset with a sensitivity of 0.5716, specificity of 0.7882, average score of 0.6799, harmonic score of 0.6626, and total score of 0.6756.

Automobiles are the world’s primary way to travel, which leads to a constant race that automobile manufacturers are improving their vehicle performance by reducing weight. This paper introduces the values of density, yield strength, tensile strength, tensile modulus, flexural strength, and other properties of some new materials such as carbon fiber composite, magnesium alloy, aluminum alloy, high-strength steel, and carbon ceramic. Their drawbacks and advantages in vehicle industries and their potential development are presented in this paper.

To solve the problems of energy and environment, after experimentations and selections, hydrogen steps into the limelight; we call it "the ultimate energy source for the development of human society," and fuel cell technology is an essential step of pursuing the recyclable hydrogen energy. Fuel Cell is viewed as one of "The ideal power generation devices in the 21st century"; it has a high energy transformation efficiency, and the electricity generation process has a low environmental impact. If the fuel is being provided, fuel cells can continuously provide electricity, which can likely be applied in power plants, electric vehicles, electronic devices, mobile communications, and space facilities. This work focuses on Alkaline Fuel Cells. The oxidation-reduction process will be more straightforward in alkaline-based electrolytes than in acidic electrolytes, and the alkaline system will also perform better under room temperature. Besides, Alkaline Fuel Cells (AFC) can use non-platinum catalysts, so the cost is lower than the other Fuel Cells. Thus, designing a Hydrogen-based AFC is what's being focused on in this article. However, AFC has its disadvantages too. First, the CO2 in air and fuel gas must be cleaned up because of the alkaline electrolyte. Furthermore, water is a by-product of the electrochemical reactions inside AFCs. Therefore, AFC performance will be seriously affected if the water doesn`t expel. Another issue that current AFCs face is the problem of CO2 poisoning. To address the above questions, we approached it from a thermodynamic perspective, compared the basic structure, material, drainage method, etc., of multiple alkaline fuel cells, and selected the more efficient ones that will be more effective for developing the AFCs.

Linear algebra is an important mathematical method used to solve real-life problems such as optimization. This paper chose the Travelling Salesman Problem as the topic. TSP Problem had a lot of different methods and algorithms. In this paper, the principle of each method is explained, and the comparison with the test data is shown at the end of the paper. Matlab was used to support the research. The source codes of each method were uploaded to Github. According to the test data, the branch-and-bound method performed the best because it takes the least time to run and can handle the most data. TSP Problem is widely used in real life, such as transportation. The faster the code, the better the performance of the program.

In these years, with the continuous development of 14nm and 7nm chip processes and the increasing popularity of computers, cell phones, and smart homes, the growing market of electronic products and ultra-fine research for the reliability of the chip requirements gradually increased. The method of reducing power consumption in the new context of new technology is called the chip field die-cut concern. This paper mainly summarizes two aspects of power consumption sources and methods to reduce power consumption, to provide a reference basis for future research directions: First, the major sources of power consumption are static power consumption and dynamic power consumption. The primary sources of static power consumption are sub-threshold leakage and gate leakage current. This paper also mentions some other leakage currents, such as reverse bias PN junction current and induced leakage current, which can be more carefully considered when exploring methods to reduce power consumption. A more comprehensive consideration when exploring ways to reduce power consumption. Dynamic power consumption is mainly divided into Switching power and Internal power and short-circuit power. Then we summarize the existing methods which can reduce power consumption: Clock gating (including Clock gating without a latch and Clock gating with a latch), Dynamic voltage and frequency scaling, multi-supply and multi-voltage technology, Power gating and multi-threshold voltage. These methods are from a proprietary perspective to reduce power consumption.

In the past decade, two players have overshadowed others in soccer. Who is better, Lionel Messi or Cristiano Ronaldo, has been debated for over a decade. Unlike basketball, the low-scoring nature of soccer dictates that one usually cannot visually conclude the game. Most people discuss who shines in terms of statistics, but there is no way to know the goal-scoring preferences of either man. This paper explores the goal-scoring ability of the two men in different situations to prove who is more complete based on the goal-scoring records of the 2020-2021 season and the data required for expected goals (xG). The study results prove that Messi is more dominant with long-range shots, and Ronaldo scores goals in all visible ranges. This paper introduces a new method of comparing Messi and Ronaldo and uses it as an example to develop a comparison that applies to all players.

This paper has introduced and examined some methods to generally distinguish whether an exoplanet is terrestrial, with the help of available data. These methods include analysing the exoplanet's orbital period, size, distance from the star, and density. By comparing these properties with the known characteristics of terrestrial planets, we can make an informed judgment about whether an exoplanet is terrestrial or not. Using the transit method, we can find an exoplanet and determine its orbital period and size. By applying Kepler's Third Law together with the radial velocity method, we can calculate the star-planet distance and the planet's density, respectively. However, it is important to note that these methods have their limitations and uncertainties. The transit method is limited by the probability of observing a transit due to randomly oriented planet systems, and the radial velocity method is only highly receptive to detecting massive planets in close proximity to the stars they orbit. Additionally, certain astrophysical phenomena can lead to false positives in exoplanet detection. Despite these limitations, the methods presented in this paper provide a foundation for determining the likelihood of an exoplanet being terrestrial. As technology advances and our understanding of exoplanets expands, we will continue to refine these methods and develop new ones, improving our ability to explore terrestrial planets and potentially discover life beyond our solar system.

With the rapid development of computer technology, the concept of computer vision has been proposed. Since then, many object recognition methods have been developed to lay the foundation for computer vision. Object recognition is vital in various computer vision applications, such as autonomous driving, surveillance systems, robotics, and other areas. The You Only Look Once (YOLO) model has gained significant attention due to its ability to achieve real-time object detection and localization in images and videos. This paper comprehensively reviews real-time object recognition based on the YOLO model. We discuss the YOLO architecture's underlying principles and advantages over traditional object detection methods. Then, according to the article by Joseph Redmon, the inventor of YOLO, the benefits of each version of the YOLO model and the performance optimization compared to the previous work are briefly introduced in the order of release. Furthermore, this paper explores its applications in different domains.

Machine learning is a highly effective instrument in constructing models that can expeditiously produce accurate prognostications. As the complexity of integrated circuit design continues to increase and process nodes continue to evolve, and physical design faces more challenges from modeling and optimization. To address these challenges, machine learning has been introduced into physical design. Thus, in this paper, we discuss the application of machine learning in physical design, covering topics such as Clock Tree Synthesis (CTS), Placement and Routing, IR-Drop and Static Timing Analysis (STA). The essay explores how machine learning can be used to overcome challenges in these areas, such as reducing peak current and clock skew in CTS, optimizing placement parameters and decision-making, predicting routability and reducing IR-drop effects. This paper also discusses various machine learning techniques (ML), such as reinforcement learning, convolutional neural networks and transfer learning. To conclude, we provide insights into how machine learning can be applied to improve various aspects of physical design.

It seems true that we can almost always determine the position of a specific point when a set is given. Namely, we could assert whether this point lies at this set's interior, boundary, or exterior. However, this is not always the case in constructive mathematics. In this research, we will show that it is generally impossible to algorithmically determine whether a rational point lies in the interior of a closed productive set or on the boundary of it. We conduct our proof by making contradictions. Firstly, We used an unextendible algorithm to construct a rational point and a closed set on the natural line. Secondly, we reformulate the assumption "we could decide whether the point lies in the interior or on the boundary of a closed set” to “we could determine the program will eventually print 1". Thirdly, we constructed an extension of the program to all the positive integers, which is a contradiction to our assumption. Hence, we concluded that it is impossible to figure out the position of the rational point algorithmically.

Photovoltaic technology is a technology that uses the photoelectric conversion properties of semiconductor materials to convert solar energy into electricity. Photovoltaic technology is a kind of renewable energy technology that does not produce pollution and greenhouse gas emissions and has many application prospects. According to data, from 1985 to 2017, the cumulative total installed photovoltaic capacity globally exceeded 400 GW and is expected to grow in the next few years. Photovoltaic technology has become an essential part of renewable energy worldwide. Photovoltaic cells are the core equipment of photovoltaic technology. There are mainly monocrystalline silicon, polysilicon, amorphous silicon, organic solar cells, and other types. Among them, monocrystalline silicon photovoltaic cells have high photoelectric conversion efficiency, but high cost, mainly used in high-end applications; Polysilicon photovoltaic cells are cost-effective and used primarily in large-scale photovoltaic power station construction; Photovoltaic technology can also be used in mobile chargers, solar streetlights, solar pumps, and other aspects. In addition, photovoltaic technology also has a wide range of application prospects in urban rail transit, automobiles, robots, and other fields also appear. Although photovoltaic technology has many advantages, there are still some problems, such as panel cost, reliability, stability, and storage of photovoltaic power generation. Solving these problems requires continuous upgrading and development of technology. With the progress of technology and the support of policies, it is believed that photovoltaic technology will continue to develop and make more significant contributions to global energy transformation and sustainable development.

This paper describes femtosecond pulsed laser technology and related applications. The focus is on two core femtosecond pulsed laser technologies: femtosecond pulsed laser generation and amplification. In the generation of femtosecond pulsed lasers, mode-locking techniques, Kerr-lens mode-locking, and semiconductor saturable absorber mirrors are presented; in the amplification of femtosecond pulsed lasers, chirped-pulse amplification and coherent synthesis techniques are presented. This paper analyses the applications of femtosecond pulsed lasers in both the biomedical and manufacturing sectors and gives the development trends as well as the challenges of femtosecond laser technology. Femtosecond lasers are now used in a wide range of applications. Femtosecond pulsed lasers will develop in the directions of high power, miniaturisation, intelligence and precision. Laser tweezers will become the new direction of development in the future.