Textual analysis and the Entropy-TOPSIS method are used in this research to create a measure of corporate environmental protection, and multiple regressions are used to find out how digitalization affects corporate environmental protection. The research sets up a theoretical framework for how corporate digitalization affects environmental protection and looks into how external financing constraints and an organization’s own financial position play a role in the middle. The research then looks at how outside factors like the business environment of the market and the level of competition in the industry affect the relationship. Using a threshold regression approach, the research also examines the change in the impact of digitalization on environmental protection after investor sentiment crosses the threshold from the distinct perspective of investor sentiment. Our research provides theoretical support for environmental protection by corporations and government policy direction. As human society has become more productive, it has created a lot of material and spiritual wealth. Nevertheless, productive activities inexorably consume resources and impose a certain burden on ecological cycles . The EarChina is currently part of the Industry 4.0 era. Currently, information and data are used as a means of production to assist the market in functioning more efficiently. China’s State Council published "Opinions on Building a More Perfect Institutional Mechanism for the Market-based Allocation of Factors" in 2020, which proposes to highlight the role of information technology and guide the formation of a market that uses data as a factor of production, thereby injecting new life into the national economy. The digital economy refers to a set of economic activities that use modern information networks as a carrier, digitize information in the production and operation of enterprises as a factor of production, improve the efficiency and accuracy of decision-making through modern information technology, and promote economic growth by enhancing production efficiency and industrial transformation ,6. ChinaWhat variables influence environmental protection? The majority of research has focused on corporate finance , industrMoreover, what are the consequences of the digital economy? The digital economy will bring about changes in production efficiency, industrial structure, and regulatory strategies. The digital economy employs the Internet information technology of the third industrial revolution to use information data as a means of production, thereby making the production process more transparent and enabling production departments to better control the production process . After tHow, then, does the digitalization of enterprises impact environmental protection from a macroeconomic and microbusiness perspective? There is still space for extensive research expansion in this field. This investigation begins with an introduction. It describes the history of China’s digital economy and environmental governance. In addition, we examine the pertinent literature in this section. The theoretical analysis and research design constitute the second section. We conduct a theoretical analysis of how the digitalization of businesses impacts environmental governance and develop a model based on the hypothesis. The third section is devoted to the research methodology. Here, we present the foundation for data selection and variable measurement. The fourth section contains the empirical test results. Here, the hypotheses are evaluated, and additional analysis is offered. The fifth segment is the discussion, and the sixth section concludes with an outlook based on the paper’s limitations.The following are the minor contributions of this study: (1) We have developed an indicator to measure a company’s environmental protection efforts. We believe that a company’s environmental protection should include the significance it places on environmental protection, the actions it has taken, and the results it has achieved. This study combines text learning with the Entropy-TOPSIS method to develop an environmental protection index. (2) The commercial impact of digitalization on environmental protection was investigated. The impact of digitalization on environmental protection has received less attention in prior research. Even when scholars have focused on this aspect, they have typically analyzed it from a provincial or urban perspective. This paper examines this topic from the enterprise’s micro perspective.In business, the digital economy refers to the digitalization of the enterprise. When a business turns digital, digital information systems can collect and organize production data more quickly, and decision support systems can assist managers in making more accurate decisions and allocating resources more efficiently. The optimization of business processes and the reorganization of production methods have given companies a new lease on life, giving them the confidence to choose initiatives with a low short-term return but long-term benefits. Environmental protection is of particular importance to businesses. However, environmental protection conflicts with the "rational economic man" character of business in terms of profitability. Funding constraints and financial difficulties are significant barriers to businesses fulfilling their responsibilities as "social men" . The entThe new growth theory focuses on forces within the economic system and says that these internal factors are what cause economic growth . BusinesUnder the consensus theory, humans and nature are required to coexist in harmony. The economy, society, and environment are all interconnected, and alterations to one factor will have an impact on the others. Without the support of the environment, the orderly development of the economy and society is impossible . DigitalUltimately, companies are profit-driven. Environmental protection necessitates production method modifications and technological advancement. These changes have a significant impact on the business and necessitate substantial capital expenditures, which are a barrier to environmental protection . The entIn conjunction with the above theoretical analysis, this study presents the following hypotheses.H1: Other things being equal, the digitalization of enterprises can contribute to environmental protection.H2a: Other things being equal, the digitalization of companies promotes environmental protection by improving their internal financial situation.H2: Other things being equal, the digitalization of the firm promotes environmental protection by way of the degree of external financing constraints.We have drawn a flow chart based on the theoretical analysis above, which the reader can see in This study’s sample is made up of Chinese A-shares from Shanghai and Shenzhen from 2008 to 2019. Since the end of 2007, China has had a new accounting standard that requires Chinese companies with shares on the stock market to disclose accounting information in a standard way. Late in 2019, COVID-19 broke out in China, which has caused a lot of economic and social trouble. Under this scenario, we believe that the financial data of companies may become skewed. We therefore use 2019 as the study’s cutoff year. Moreover, the selected data for this investigation has been processed as follows: (1) Excluding companies with two consecutive years of losses (with "ST" in the stock abbreviation) and companies whose listing has been suspended for the purpose of special transfers ("PT" in the abbreviation). In the event of delisting or transfer, these companies are likely to have anomalous financial data, which may influence the empirical results. (2) Excluding financial and insurance companies. Their industry codes are J66, J67, J68 and J69. Which have unique production and operation models and financing access compared to other industries. (3) Exclude samples with absent or discontinuous data. (4) Exclude businesses that have been publicly traded for less than a year. (5) To avoid the effect of extreme values, we winsorize the data by the first and last 1%. Following the preceding operations, we obtained 3,757 companies and 31,315 data items in total.The majority of the data for this research came from the China Economic and Financial Research Database (CSMAR). Using Python for textual analysis, the section involving word frequency statistics was examined. In addition, Excel 2021 and Stata 17 were employed for data processing and analysis.We have listed the main variables used in this study in Formal pairs of public reports, like an organization’s annual report, social responsibility report, and environmental report, are important ways for the organization and its outside stakeholders to talk to each other. These reports not only reflect the business status of the enterprise, but they can also communicate its strategic direction . This stThis study measures the degree to which businesses are digital based on the amount of money they invest in digital transformation. The feature word mapping in Some academics use the number of times a keyword appears as a metric because it shows how a company runs its business ,41. In tToo many omitted variables may have an effect on the residuals of the multiple regression equation, leading to endogeneity issues. We utilize the research of Liu & Liu (2022) and Zhang et al. (2022) to include control variables that reflect the financial position and management characteristics of the firm, such as the balance sheet ratio and monetary situation ,42.β1 is significantly positive, it indicates that digitalization of enterprise is conducive to increasing the firm’s environmental protection.To examine the impact of digitalization of enterprise on enterprises’ environmental protection, the following model is developed. If EPi,t denotes environmental protection of firm t in period i. DGLi,t denotes the digitalization of firm t in period i. controlsi,t denotes the control variables of firm t in period i. Please see The descriptive statistics for the primary variables are displayed in Multiple regressions confirmed in the preceding section that the degree of digitalization of the firm (DGL) can considerably contribute to the firm’s environmental protection (EP). This study will conduct a robustness test with replacement explanatory variables and a GMM model utilizing the 2SLS method.This study executes robustness tests by varying the method of measuring explanatory variables. The test results are displayed in column (1) of Businesses can get out of their financial binds and improve their financial situation through digital transformation, which gives them enough money for environmental management. Protecting the environment is important for the long-term growth of human society and for each person’s well-being. Companies have a moral duty to do their part to protect the environment as a social environment. Protection of the environment, on the other hand, is a way for businesses to internalize external diseconomies into the cost of doing business . As an eUsing stepwise regression, this study looks at how the role of external financing constraints and internal financial position can act as a mediator. This paper refers to Hadlock and Pierce (2010) to look at how external financing constraints act as a mediator . HadlockIn the last section, we looked at how a company’s level of digitalization (DGL) affects environmental protection (EP) by way of the ways it sends information. What external factors can affect the relationship between the two parties? As the aggregate of the external environment in which firms transact, the market business environment influences all aspects of a firm’s initiation, production, and transactions . Li KeqiThe degree of competition in an industry refers to the intensity of competition between enterprises in the industry at a particular time. In industries with greater levels of competition, firms frequently encounter a plethora of rivals with the same business strategy. To make sure their products sell well, these companies often compete with each other in a zero-sum way, such as by lowering their prices to take the market . When thWhen it comes to outside monitoring of businesses, a high level of investor confidence is not good for the smooth running of businesses. Behavioral finance theory says that investors don’t act in a logical way and that their mental states show up in how they act . In otheIn this paper, we use Baker & Wurgler’s (2007) method of decomposing the Tobinq value to measure investor sentiment . Table 9The digitalization of businesses can contribute to environmental preservation by improving the financial situation and reducing the level of financing restrictions. When digitalization is used in businesses, this advanced technology can be used to cut costs, increase efficiency, and improve production methods . This prThe government must foster the development of the digital economy and provide mental guidance. The growth of the digital economy can help human society integrate resources more effectively and establish a more rational communication platform. In addition, prior to constructing a digital platform, the government should perform an effective role of external oversight to ensure that the platform is fundamentally sound. Additionally, government policies and regulations can affect the preferences of the capital market. China’s government can play an effective role as the "invisible hand" by urging businesses to protect the environment in order to guarantee their own survival and growth. The dangers of over-concentration should also be brought to the attention of the government. Too much concentration can lead to limited resources being held in the hands of a few, making it impossible to maximize the use of resources. When bullish, investors must also "throw frigid water" on the market. High investor sentiment can be advantageous for companies’ capital costs, but the resulting absence of regulation can prohibit the use of digitalization-generated resources for environmental protection.This study uses text analysis and the Entropy-TOPSIS method to come up with an indicator for judging how well companies take care of the environment. After doing the necessary work on the data, we got 31,315 data samples and came to the following conclusions: (1) Enterprise digitalization contributes to environmental protection. As digitalization expands, the production and management practices of businesses are altered. With the implementation of modern information technology, it is anticipated that enterprise output per unit will increase substantially, while energy consumption will decrease. This will assist businesses in protecting the environment. (2) The market environment and level of industry competition perform a moderating role. In a healthy business environment, production, transactions, and distribution in the marketplace typically flow smoothly and orderly. The environmental repercussions of digitalization are effectively communicated, and access to financing is ensured. The increased market competition will inevitably have an effect on the day-to-day operations of the business. In a highly competitive market, businesses must concentrate on their survival (competition). In this instance, digitalization’s resource and management advantages are prioritized for product competition. Not immediately profitable projects, such as environmental protection, are frequently neglected. (3) Digitalization is not perfect for businesses. When investor sentiment is so high that external oversight is compromised. The convenience of digitalization and the vast quantity of available information can serve as a cover for managers’ "self-interested" behavior. It can even interfere with business decisions.This study’s sample selection has some limitations. For the sake of data consistency, we use the Chinese Shanghai and Shenzhen A-shares from 2008 to 2019 as the sample, omitting data from the post-COVID-19 period. Taking into account the period requirement of the research methodology and the schedule of financial reports issued by Chinese companies that are publicly traded. After the listed companies have produced their corporate financial statements for 2022, academics will be able to examine the effect of corporate digitalization on environmental protection in terms of COVID-19.S1 Fig(DOCX)Click here for additional data file.S1 Data(DTA)Click here for additional data file.


Complete coverage path planning requires that the mobile robot traverse all reachable positions in the environmental map. Aiming at the problems of local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm, a complete coverage path planning algorithm based on Q-learning is proposed. The global environment information is introduced by the reinforcement learning method in the proposed algorithm. In addition, the Q-learning method is used for path planning at the positions where the accessible path points are changed, which optimizes the path planning strategy of the original algorithm near these obstacles. Simulation results show that the algorithm can automatically generate an orderly path in the environmental map, and achieve 100% coverage with a lower path repetition ratio. Along with the unceasing progress of science and technology, all kinds of mobile robots have been imagined, and these robots are applied to many fields such as room cleaning ,2,3, expWith the rapid development of artificial intelligence-related technologies, many intelligent algorithms are used for the complete coverage path planning domain , such asOn the other hand, the rapid development of reinforcement learning and deep learning brings a whole new solution to path planning. Ai et al.  presenteAmong all aforementioned algorithms, the complete coverage path planning algorithm has the fastest calculating speed, but the path planning repetition ratio is high in complex environments; intelligent algorithms considering environmental information can plan orderly paths, but have the problem of long calculating times. In response to the issue and research contents, this paper presents a biologically inspired neural network algorithm based on Q-learning, which proceeds with path planning based on the biologically inspired neural network algorithm, and when it comes to the position change of a reachable path point, processes path optimization by adopting Q-learning to avoid falling into a locally optimal solution, thus reducing the path repetition ratio, while still ensuring complete coverage, achieving the desired path planning results. The rest of this paper is organized as follows. In The complete coverage path planning issue can be described as follows: on an environmental map, the mobile robot sets off at the starting point, avoids obstacles, passes by all the reachable position points in an orderly way on the environmental map, and reduces path repetition as much as possible. The biologically inspired neural network algorithm has many advantages such as the following: simple calculation, no need for model training, and the mobile robot can plan a 100% coverage ordered path under the restricted condition of no motion template or energy loss function; therefore, it is widely used in the indoor robot complete coverage path planning domain.mC is the membrane capacitance, kE, naE, pE, kg, nag, and pg are electric potentials and the electric conductance of potassium ions, sodion, and the passive leakage current in cell membranes, respectively. Grossberg made a summing based on Equation (1), and set up the neural dynamic network model, and created the famous shunting equation:A, B, and D are the attenuation ratio, the upper bound, and the lower bound of the neural activity value, respectively; Physicists Hodgkin and Huxley developed the famous biological nerve cell membrane circuit model  to explaYang et al.  set up aThe above Equation (3) can ensure that the positive neuronal activity can be spread into the entire neural network by the connection of neurons, and the negative neuronal activity can be limited to the nearby obstacles to block signal transmission. Therefore, using the fractional flow formula to define the model, the mobile robot can automatically choose the next moment’s moving direction by the neuron activity value of the neural network, then complete the path planning. The above neural network model can be described in Yang et al.  proceede(a)Build a neural network model correlated with environmental information, and set up external excitation and inhibiting input according to obstacles in the map and the positions of reachable points;(b)Iteratively calculate each neuron’s activity value according to the neural network model and external input;(c)The mobile robot selects the next moment’s path point according to the activity value of the neuron adjacent to the current position and steering angle variation;(d)When the robot falls into a dead zone, which means that around its current position are obstacles and the path point has been passed, the robot calculates the activity value of the neuron grid adjacent to the current position; if the values are all less than the value of the current position, the robot remains still until there is one path point’s activity value greater than the current position value; then the robot moves into the position of the next moment’s path point;(e)The mobile robot considers the next moment’s path point as the current position, then estimates if it will accomplish the complete coverage of the environmental map. If the answer is yes, then it is the end of the algorithm; if the answer is no, then go back to step (b).According to the above path point selection method, the biologically inspired complete coverage path planning process is as follows:According to the above content, a flow chart of the biologically inspired complete coverage path planning algorithm based on Q-learning is shown as follows :The biologically inspired neural network algorithm has the merit of simple calculation; however, the repetition ratio of planned paths is very high in an environment with obstacles distributed in complexity; therefore, this paper presents the improved algorithm based on Q-learning, which optimizes the path combined with the environmental map.According to the above algorithm principle, build a As shown in Q-learning is a classic no model reinforcement learning algorithm, which is based on the theory of Markovian decision processes. Markovian decision processes can be defined by a tetrad Q function, in Q-learning for evaluation. The function value of Q function, and there is no need to consider the state transition probability Q function from all the motions Q function iterative formula based on Behrman criterion is as follows:There is a state action function, Q form whose purpose is to store the Q value that corresponds to all the state actions of the intelligent body. The common action selection strategy of Q-learning is the Q form when the motion selection has Q form comes to convergence, and then the intelligent body can obtain each state’s optimal action through the Q form, which is the output of the optimal policy. The algorithm flow chart of Q-learning is shown in During the learning process, Q-learning sets up a Q is very slow, and sometimes it does not converge due to big environmental maps and sparse learning rewards, so entirely applying the Q-learning algorithm to a global map will need to be improved. The efficiency of the original biologically inspired neural network algorithm is high; however, it does not consider map features near obstacles or around the planned path, so applying the Q-learning algorithm on the area near obstacles and proceeding with strategy optimization can improve efficiency greatly. In order to increase the ratio of convergence of Q-learning, only apply Q-learning when the mobile robot detects that the reachable position points are increasing, and apply the biologically inspired neural network algorithm on the other areas where there is no change in the reachable position points, which can plan the path rapidly and orderly, as shown in Reinforcement learning has a strong ability for learning and decision making; however, applying a reinforcement learning algorithm on complete coverage path planning will create a problem: the convergence speed of the function In Q form convergence of this algorithm, the mobile robot is encouraged to change moving direction according to the position of obstacles when the reachable position points increase. The setting of the greedy strategy of the Q-learning process is shown as follows, where In order to reduce algorithm complexity and generate more ordered paths, make a mobile robot that can only move towards up, down, left, and right (four directions) for reinforcement learning and the biologically inspired neural network algorithm. In order to accelerate the Q form based on the probability of the exploring factor When the mobile robot chooses a moving direction, determine an exploring factor According to the above content, a flow chart of the biologically inspired complete coverage path planning algorithm based on Q-learning is shown as follows :(a)Build an environmental map, and then initialize the parameters of the biologically inspired neural network and Q-learning;(b)Estimate if the reachable position points number of the mobile robot is increasing; if it is not increasing, proceed to step (c); if it is increasing, proceed to step (d).(c)Calculate each neuron’s activity value of the neural network according to the obstacle distribution in the obstacle environmental map and the planned path based on Equation (3), and then the robot moves according to Equation (6); proceed to step (e);(d)Select the next mobile strategy according to Q-learning’s greedy algorithm, proceed to moving and update Q form, and proceed to step (e);(e)Estimate if complete coverage path planning will be accomplished or not; if not, go back to step (b), and continue to plan paths; if yes, proceed to step (f);(f)Estimate if the Q form has been converged or not; if not, go back to step (a) and proceed with the next learning; if yes, then complete the path planning task, and output the path.Specific steps of the algorithm according to the above algorithm flow are as follows:In order to verify the feasibility and effectiveness of the biologically inspired complete coverage path planning algorithm based on Q-learning, proceed to the simulation experiment of mobile robot path planning on an indoor environmental map.Build a grid map according to the size of the environmental map and obstacle distribution. Set the starting point of the mobile robot. If the robot reaches all the coverage grid points, it is considered as having accomplished the complete coverage path planning.Set partial parameters of the biologically inspired neural network as follows: in Equation (3), Build a size 15 × 15 grid map according to an indoor environment, shown in The above simulation results show that the biologically inspired complete coverage path planning algorithm based on Q-learning is very effective, and it can plan a better path compared with the traditional boustrophedon coverage algorithm and the secondary region partitioning algorithm. Furthermore, this paper conducts a simulation experiment on a bigger size indoor environmental map, shown in The simulation results show that the improved algorithm is different from the traditional complete coverage path planning algorithms, which show that the improved algorithm proposed in this paper can change planned path direction actively according to the distribution of obstacles on the map. Although the path generated by the improved algorithm has more turns, the path repetition rate is lower, which reduces the energy loss of the mobile robot at work. The improved algorithm has the characteristics of the high computational efficiency of the biologically inspired neural network, and the Q-learning algorithm is introduced to optimize the turning strategy of the path near the obstacles, so that the planned path is more orderly.This paper presents a biologically inspired complete coverage path planning algorithm based on Q-learning, which uses Q-learning in areas near obstacles to improve the algorithm of path planning strategy based on the original biologically inspired neural network algorithm. The improved algorithm solves two problems: one is the complex paths and high repetition coverage ratio of the biologically inspired neural network algorithm; the other is avoiding the low computational efficiency of using only Q-learning on complete coverage path planning. The results indicate that the improved algorithm can generate a complete coverage path in an indoor environment without a motion template or energy loss function, and while reducing the path repetition ratio effectively.This paper conducts research on robot complete coverage path planning in a simpler indoor environment; however, in actual applications, the environment is more complex in general. The Q-learning algorithm tends to find the globally optimal path, so the Q form converges very slowly if there are no constraints. Therefore, there are many involved possibilities during the complete coverage process, and the earlier reinforcement learning exploration stage costs much time, so the calculation speed is very slow and convergence is very difficult in complex and large-scale scenarios. Therefore, the next research content is to expand and improve the algorithm. For example, adding a number of scene restrictions on Q-learning, and optimizing the Q form initial value according to the data on the neuron activity value, may allow this algorithm to achieve complete coverage path planning under the conditions of complex environments such as multi-obstacle scenarios and large-scale scenarios.


During the downswing, individuals with KIH+ exhibited a smaller hip flexion angle, smaller ankle abduction angle, and larger ankle adduction/abduction range of motion (ROM). Moreover, there was no significant difference found in the knee joint moment. Athletes with a history of knee injury can adjust the motion angles of their hip and ankle joints  to minimize the impact of changes in their movement patterns resulting from the injury.Although prevention is better than treatment, after a knee injury occurs, the adjustment of the movement technique back to the posture before the injury and the restoration of accuracy is very important for professional and amateur players. This study aimed to compare the differences in lower limb mechanics during the golf downswing between those with and without a history of knee joint injury. A total of 20 professional golfers with single-digit handicaps were recruited for this study, 10 of whom had a knee injury history (KIH+), while another 10 players were without a knee injury history (KIH−). From the 3D analysis, selected kinematic and kinetic parameters during the downswing were analyzed using an independent samples  Inappropriate swing movements will not only affect hitting quality, but also increase the incidence of sports injuries. The golf swing is a complex movement, requiring the coordination of all of the parts of the body in order for it to be executed skillfully . AlthougDue to the specificity of this sport, players are required to strike the ball precisely on the target, which can be physically demanding . It was Exploring knee joint changes as an index during the golf swing through sports biomechanics allows for the accurate quantification and judgement of swing characteristics ,16. The Previous research has reported that knee joint injuries from playing golf are mainly caused by the tibial rotation of the lead leg ,24. ReseTherefore, the purpose of this study was to compare the axial angular displacement and moment of each lower limb joint of the lead or left leg for a right-handed golfer during the swing between two groups of highly skilled golfers with and without a history of knee joint injury. We sought to understand the posture differences in peak angle and peak moment. It is our hope that the results will serve as a reference for those with a history of knee joint injury to make corrections and improvements in their movement techniques.We hypothesized that the group without a history of knee injury would exhibit smaller joint angles and greater knee joint moments during the golf swing. When injuries cause issues, it is common for the golfer to introduce compensation motions. If the knee is where the injury occurred, this compensation could actually change the way the golfer moved their ankle joint.A total of 20 professional golfers with single-digit handicaps were recruited in this study, 10 of whom exhibited a chronic knee injury history resulting from playing golf (KIH+)  while another 10 players were injury-free (KIH−) . Sample size calculations  revealed that a sample of twenty participants would be sufficient to detect a difference between groups, with a statistical power of 1 and Type I error probability, associated with the test of the null hypothesis, 0.05. All of the participants were right-hand dominant, that is, their lead leg was the left leg. They had not experienced lower limb musculoskeletal injury or nervous system injury six months prior to the study implementation. The participants signed an informed consent form before the start of the experiment. The study was approved by the relevant institutional review board, in line with the principles of the World Medical Association Declaration of Helsinki, and informed consent from all of the participants was obtained.A motion capture system  with 11 Eagle digital cameras was used to capture reflective marker balls for three-dimensional marker tracking . The samThe participants first filled out an injury survey form. After confirmation, they carried out dynamic warm-up static stretching  for 10 mCortex motion analysis software  was used to mark the trajectory of the three-dimensional lower limb kinematics and kinetics during the downswing stage. The selected data were filtered using a Butterworth fourth-order low-pass filter to remove high-frequency noise, and the cut-off frequency was set at 6 Hz . The participants’ general information  was inputted to construct a human body calculation model with body segments as rigid bodies, and relative angles were obtained using the center of the joint as a fixed point. These models were created using information obtained from the attached markers, ground reaction forces, and anthropometric data. The kinematic model involved identifying the rotation center of each lower extremity joint , establishing an anatomical reference system for the thigh and shank segments, and calculating the angles of the hip, knee, and ankle joints using the Euler angle method ,34. The The kinematic changes in the knees and ankles at the downswing stage  were calculated. The joint displacement angles, range of motion (ROM), and joint moments of the lower limbs in three axial directions in the swing process were also calculated  and the t-test. The statistical significance level of this study was set at 5%. Cohen’s value was calculated to assess the effect size [The statistical analysis and calculations were performed using SPSS statistical software . The results were expressed as the mean ± standard deviation (SD). Normality was evaluated for each of the dependent parameters utilizing the Shapiro–Wilk test. The peak values and ROM of the kinematics and kinetics of the hip, knee, and ankle joints of the lower limbs in different anatomical directions  in the swing process were examined and compared between the KIH+ and KIH− groups using an independent samples ect size .t-test. t-test, no significant differences were found in the knee joint moment between the KIH group and the KIH− group.The kinetics results are shown in t-test, it was however found that the KIH+ group had a smaller hip flexion angle and ankle abduction angle, but a larger ankle adduction and abduction ROM than the KIH− group. The statistical results indicated that there was no significant difference in the knee joint moment between the two groups.Through the independent samples The purpose of this study was to explore the influences of a lower limb injury history on the lead foot during the golf downswing. The results indicate that compared with the KIH− group, the KIH+ group had a larger hip flexion angle, smaller ankle abduction angle, and larger ankle adduction and abduction ROM. The results partially conform to the research hypothesis.In the biomechanics literature, there has been little relevant sports science research on the effects of injury history on movement strategies. The results of this study can effectively provide information on relevant sports biomechanical parameters and explore the trends of swing pattern changes and differences in those with and without a history of knee joint injury. Previous studies reported the golf knee load ranged from being similar to a stair descent, to tennis serving or jogging, and concluded that the peak load generated in the golf swing was not enough to cause immediate injury and that the risk of knee joint injury was caused by repeated swings over a prolonged period . On the However, through the descriptive statistics used in this study, it was found that the deviations in the ankle adduction/abduction angle ROM at the downswing stage in the KIH− group were lower than those in the KIH+ group. Therefore, when there was no knee joint injury, the player’s hitting strategy in the transverse plane was identified as being more stable with less ROM. Previous studies have suggested that individuals with knee osteoarthritis may prefer hitting the ball from a farther distance . TherefoThis study demonstrates that the KIH+ players used more hip flexion and ankle adduction/abduction angle ROM than the KIH− group. A previous study showed that the range of knee axial rotation could result in poor contact locations at the edges of the polyethylene surface of a total knee arthroplasty (TKA) in golfers, potentially leading to chronic pain and prosthesis damage . Past stSports injuries often affect sports performance , and manThis study suggests that athletes who have recovered from knee injuries should be mindful of their hip flexion angle, ankle abduction angle, and their ROM for ankle adduction and abduction in order to minimize the impact of any posture changes resulting from the injury. In addition, it is also suggested that it is necessary for golfers to undergo long-term sports biomechanical testing, which can be used not only to prevent sports injuries, but also to recover from injuries through these data and to identify problematic feature points to improve their sports performance.


This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 μm × 710 μm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure. The search for extraterrestrial life is one of the eternal themes of deep space exploration, and it is also a major scientific problem that still needs to be solved. How to use limited means to detect the unknown world and how to use limited technology to evaluate unknown life forms are the scientific problems that human beings need to solve at this stage. Spectral detection technology is a primary means for human beings to understand the universe at this stage. The key to finding extraterrestrial life is to find direct evidence (biosignature) that life has existed. To find life outside the earth, we must understand the origin, evolution, and residual biological characteristics of life on the planet. Taking Mars as an example, NASA continues to promote the exploration of Martian life or its imprints ,2,3, amo10 times larger than the Raman cross-section. Even if the concentration of these substances is shallow, the fluorescence background they produce will be stronger than the Raman spectrum produced by the unknown substance to be tested. Therefore, it is difficult to predict the composition and concentration of these trace impurities in the unknown substance to be tested. Raman spectroscopy technology is a potentially powerful means to find the life evidence of ancient microorganisms on Mars. The Raman laser spectrometer (RLS) instrument of the ExoMars rover, built by ESA in collaboration with Roscosmos, will also use Raman technology [Raman spectroscopy is a non-invasive, non-contact molecular spectrum detection technology. Light irradiation on the object’s surface will cause elastic and inelastic scattering. The wavelength of the elastic scattering light is the same as that of the incident excitation light. The wavelength of the inelastic scattering light is usually longer than that of the excitation light. There is a nonlinear light scattering effect in this series of inelastic scattering called the Raman light scattering effect. Raman spectroscopy results from the interaction between photons and optical branch phonons . Fluoreschnology  and the chnology . Raman achnology , phylloschnology , and jarchnology . At preschnology , which cchnology ,17,18. SChoosing a shorter or longer excitation wavelength is an effective strategy to avoid fluorescence interference. Still, a shorter excitation wavelength (deep ultraviolet spectrum) means higher optical design and processing requirements, while a longer excitation wavelength (infrared spectrum) means lower excitation efficiency and Spectral resolution. Therefore, excitation light sources with visible spectral bands still have specific application potential in practical applications. To mine more spectral information in the limited spectral data, we propose a processing procedure to evaluate organic matter using the Raman baseline and combine spectral image fusion technology to detect the spatial distribution of organic precipitation. The separation of the Raman spectrum and Raman baseline is realized through the baseline estimation and denoising with sparsity (BEADS) algorithm. First, according to the dark color characteristics of stromatolite organic precipitation, the influence of organic content on the Raman baseline is evaluated. Then, the contribution of organic fluorescence in the Raman baseline is explained. Finally, the characterization of organic precipitation structure by the Raman baseline is realized. Our work aims to evaluate the ability of new detection technology to detect stromatolite organic precipitations using the Raman baseline combined with the Bilinear interpolation- Intensity Hue Saturation (BI-IHS) algorithm spectral image fusion technology.The stromatolite samples were collected near the Middle-Upper Proterozoic National Nature Reserve in Jizhou District, Tianjin, China. For protection, all sample collection activities have been prohibited in the Middle-Upper Proterozoic National Nature Reserve. Therefore, the stromatolite samples used in this paper are scattered outside the reserve, with relatively complete and well-preserved rock masses. The production process of stromatolite samples includes cutting, polishing, gluing, slicing, and grinding. The cutting process is to cut the unweathered rock sample into an appropriate size consistent with the glass slide. The polishing process uses 600~1000 mesh silicon carbide powder to grind the cut sample to smooth its cut surface. The gluing process uses epoxy resin to polish the ground rock sample. It is adhered to frosted glass and baked at 50 °C for 8 h to solidify and harden it. The slicing process involves using a thin film cutting machine (Petro tin) to cut and grind the glued sample into approximately 120 μm thin slices. The grinding process consists in using a micrometer to calibrate the thickness of the specimen and rubbing it onto a thin lapping plate to a standard thickness of 15 μm.−1. The signal-to-noise ratio of the spectrometer is 1000:1. The integration time of a single Raman spectrum is 100 ms. Raman mapping scanning area 710 μm × 710 μm, focus spot diameter 1 μm, scanning step 10 μm, scanning points 71 × 71.The Raman mapping device diagram is shown in Raman spectroscopy is a nondestructive detection technology which is easily affected by the environmental background light, fluorescence, and dark current and has noticeable baseline drift. Fortunately, Raman spectrum signals with the sparse spectral peaks and low pass filtering characteristics of baseline. The BEADS algorithm is suitable for solving the problem of baseline correction and noise reduction of one-dimensional signals. The algorithm models the baseline as a low-pass signal, while the spectral peak is sparse and has sparse derivatives. When a low-order polynomial is unsuitable for baseline modeling, the BEADS algorithm is superior to the polynomial modeling method. It has the characteristics of high efficiency, fast iterative convergence, and stability . BEADS hRegarding data processing, Matlab R2020a  is used for chemical analysis and scientific drawing, respectively. The schematic diagram in the article is drawn by Microsoft Office Professional Enhanced Edition 2019 PowerPoint , as shown in x, y). It is assumed that we know the value of f at the four points Bilinear interpolation (BI) is often used to improve image quality after performing spatial transformation operations . For exaThen, we perform linear interpolation in the y-direction:The Intensity-Hue-Saturation (IHS) algorithm is a mature spatial transformation algorithm that was developed very early in the history of image fusion technology ,26,27,28The IHS color space can be described by a conic space model, as shown in The diagonal of the RGB color cube coincides with the central axis of the IHS color cylindricum, as shown in The saturation component is given by:Finally, the intensity component is given by:The fusion process of the three bands of multispectral images is as follows:Step 1: Resample the low spatial resolution multispectral image to the panchromatic image size, then conduct the positive transformation of the resampled multispectral image from RGB to IHS space. Here, the bilinear interpolation method is used for resampling processing.Among them, Step 2: Moment matching is performed between the panchromatic image f and the brightness I component so that it has the same mean and variance as the replaced I component. The purpose is to reduce spectral distortion in the fused image, which can be expressed as:Among them, Step 3: Replace the brightness component I with the panchromatic image after moment matching, and perform IHS inverse transformation with the hue H and saturation S components to obtain a high spatial resolution multispectral image, which can be expressed as:Among them, Stromatolite layered deposit, mainly of limestone, is formed by the growth of blue-green algae (primitive one-celled organisms). It is well known that the parts with high organic content in stromatolites are dark in color. In contrast, the regions with white color are mainly composed of sand and less organic content, and the structures are usually characterized by thin, alternating light and dark layers. The original spectra in the Raman scanning area are arranged according to the gray scale of the microscopic image corresponding to the scanning point, as shown in The source of the Raman baseline is complex, related to the material’s physical property (the morphology of the sample) and chemical property , and other factors . With the same chemical composition of materials, the samples in the fine powder state are more likely to produce a strong, broad spectral background. In contrast, the models with compressed particles have better Raman spectral signals and weak spectral backgrounds. The main reason for this phenomenon is that the diffuse reflection of a fine powder sample will cause intense stray light rather than photoluminescence . From thThe spectral preprocessing based on the BEADS algorithm can realize the separation of the Raman spectrum and baseline. −1 are generated by the internal mode (v1), lattice modes (L), and lattice modes (T), respectively [The Raman mapping scanning area of the rock mass is shown in ectively . This paAlthough the BI algorithm has improved the mapping quality to a certain extent, the spatial resolution is not up to that of optical images. However, spectral image fusion can provide spatial resolution and spectral resolution. This part mainly uses the IHS algorithm to realize the fusion of microscopic images and Raman fluorescence mapping, as shown in Stromatolites are biological sedimentary structures formed by ancient microorganisms  that absorb, precipitate minerals, or capture mineral particles through growth and metabolic activities . TherefoCurrently, the strategy for searching for extraterrestrial life is mainly to take the Precambrian microbial fossils of the Earth as the model and to study the preservation potential and detection methods of potential microbial fossil life on Mars by analogy based on spectral technology. Stromatolites record essential clues to the existence of early life on the Earth and have extremely typical macro characteristics and microstructures. Furthermore, Mars may also have had environmental conditions for stromatolites to “grow” in its ancient history. The discovery of stromatolite-like minerals is evidence of extraterrestrial life’s existence and a vital search target for Martian rock sampling back to Earth in the future. Therefore, this study has reference value for searching for extraterrestrial stromatolite-like minerals.In the general environment where stromatolites are formed, they are mainly found in the upper section of clear water carbonate deposits. In contrast, it is not easy to find the presence of stromatolites in the muddy lower area of turbid water carbonate deposits. Therefore, the primary environmental conditions for forming stromatolites are carbonate-rich  and relatively clear media conditions. So far, the Earth is the only planet known to have given birth to life, but compared to the long process of cosmic evolution, many extraterrestrial planets have the conditions and timing to give birth to life. Although the current environment may be extremely harsh, such as modern Mars, it may have been a warm and humid climate suitable for life for a considerable period. By analogy with the evolution process and preservation mechanism of life on the Earth, stromatolites are an essential research object in paleontology and sedimentology, the preferred research object for the analogical study of extraterrestrial life, and also a unique biological sedimentary structure that is still forming from the pre-Cambrian period to the present, with a wide variety, a large number, and strong representativeness. Researching the morphology, sedimentology, and paleontology of stromatolites on the Earth has essential reference value for searching for extraterrestrial life. The Deep Ultraviolet Raman Spectrometer (SHERLOC) carried by the Persevere Mars rover uses a 248.6 nm excitation source to avoid interference from fluorescence signals on the Raman spectrum ,18. HoweIn addition, this scheme provides an effective method for mineral exploration in geological or environmental sciences and other fields of the Earth. The weak Raman spectral signal has always been a challenge in this field. Suppose the influence of factors such as ambient light, temperature fluctuations, and noise can be excluded. In that case, the Raman baseline generated by physical and chemical properties is essential in analyzing mineral properties. However, this technology also has limitations, as the conclusion that the main factor causing Raman baseline is attributed to organic matter deposition is supported by micrographs. In the practical application of deep space exploration, producing laser thin films on-site is almost impossible. Therefore, many ground simulation experiments are needed to comprehensively evaluate the impact of physical and chemical properties on the Raman baseline, especially for the Raman baseline problem caused by organic matter deposition in different minerals.In this work, we proposed a method to realize a spectral image fusion map with high spatial resolution and high spectral resolution of stromatolite microstructure. We evaluated the feasibility of the Raman baseline as a fluorescence spectrum of organic precipitation. To improve the utilization of spectral data, the BEADS algorithm is used to separate the Raman spectrum from the Raman baseline, and the corresponding relationship between the Raman baseline and the stromatolite light and dark layers is analyzed. It is found that the fluorescence of organic precipitation is the main factor that causes the enhancement of the Raman baseline index. Therefore, the spectral image fusion of spectral mapping and microscopic images is realized based on the BI-IHS algorithm, and the spatial distribution of carbonate minerals and organic precipitations are obtained, respectively.In conclusion, our research shows that the Raman baseline is not necessarily redundant information. It may contain the fluorescence of organic sediments. Under the condition that the kerogen Raman spectrum peak is not detected, the spatial distribution of organic residues can be seen by combining the fluorescence information in the Raman baseline with the mapping technology.