In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction are based on convolutional neural network (CNN) structures to capture spatial morphological information of the multichannel sEMG or based on long short-term memory network (LSTM) to extract time-dependent information of the single-channel sEMG. However, there are few methods to comprehensively consider the distribution area of the sEMG signal acquisition electrode sensor and the arrangement of the sEMG signal morphological features and electrode spatial features. In this paper, a novel multi-stream feature fusion network (MSFF-Net) model is proposed for sEMG gesture recognition. The model adopts a divide-and-conquer strategy to learn the relationship between different muscle regions and specific gestures. Firstly, a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) are used to extract multi-dimensional spatial features from signal morphology, electrode space, and feature map space. Then the learned multi-view depth features are fused by a view aggregation network consisting of an early fusion network and a late fusion network. The results of all subjects and gesture movement validation experiments in the sEMG signal acquired from 12 sensors provided by NinaPro’s DB2 and DB4 sub-databases show that the proposed model in this paper has better performance in terms of gesture recognition accuracy compared with the existing models. Surface electromyography (sEMG) is a signal graph that uses electrodes to measure muscle electrical activity from the surface of the skin, the recorded signals provide relevant information about human activities. sEMG has important practical value in clinical medicine, ergonomics, and rehabilitation medicine. Human-computer interaction methods based on sEMG are not only widely used in prosthetics , sign laAccording to the difference in electrode equipment, sEMG can be divided into sparse sEMG and high-density sEMG . Sparse The existing sparse sEMG pattern recognition methods can be roughly divided into two categories: (1) methods based on feature engineering (2) methods based on feature learning .Feature engineering methods improve information quality and density by designing features such as feature extraction time and frequency and then selecting appropriate classifiers to complete the gesture recognition task. Such as linear discriminant analysis (LDA) , principIn the feature learning method, features are automatically generated by the machine learning algorithm, therefore, the research focus has shifted from manual feature engineering to automatic feature learning. In the research on improving the accuracy and real-time performance of gesture prediction, Wei et al.  combinedIn summary, there is room to improve the recognition accuracy of deep learning-based methods on sparse EMG signals. Inspired by the studies above, we propose an sEMG gesture recognition model based on a multi-stream feature fusion network (MSFF-Net) on the DB2 and DB4 sub-datasets of the publicly available dataset NinaPro, which focuses on improving the accuracy of gesture recognition. In this model, we enrich the idea of the multi-stream convolution network and focus on the information of different muscle regions with three branches. The model uses a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) to alternately extract the morphological features of signals in different periods and the spatial features of the different number of electrode acquisition channels. Then, the early signal features and the deep features after the late multi-stream convolution network are fused in proportion and sent fusion features to the classifier to output the classification results. Experiments show that this method is superior to other existing methods in experimental data processing and recognition accuracy.The major contributions of this paper are summarized as follows:We propose a method to analyze multi-channel sEMG signals separately according to muscle regions, which reduces the influence of different muscle regions in feature extraction and strengthens the connection of signals in the same region.A novel MSFF-Net model for sEMG gesture recognition is proposed. Combined with the characteristics of the sEMG signal, the model extracts and fuses the features of the sEMG signal from the aspects of signal morphological features, electrode spatial features, and early-late stage feature fusion.We developed an experiment for the proposed model using the sEMG signal obtained by 12 sensors provided by NinaPro’s DB2 and DB4 sub-databases. Compared with similar methods, it has better recognition accuracy.The rest of this paper is organized as follows. In Section II, we introduce the sEMG signal preprocessing and describe the proposed MSFF-Net in detail. Section III presents the experiment process and the results of the proposed method. Section IV discusses BN layer order effect, early and late network weight effects, ablation studies, and a comparison of similar literature. Section V finally concludes our work.In this part, we will first introduce the multi-stream feature fusion network overall framework for sEMG gesture classification, then introduce the steps and methods of sEMG signal preprocessing, and finally introduce the composition of the multi-stream feature fusion network in detail.The overall frame diagram  shows thThe sEMG is a nonlinear non-stationary time-series signal that can reflect information related to muscle and body behavior, generated by weak action potentials generated by muscle fibers on the skin surface when the skeletal muscle contracts. Like other physiological electrical signal measurements, they are easily corrupted by noise. Three types of noise appear: power-frequency interference, white Gaussian noise, and baseline wander, making sEMG signals difficult to analyze and having a low signal-to-noise ratio. To better analyze the sEMG signal, preprocessing is required. The preprocessing process mainly includes denoising, action segmentation, normalization, and data segmentation.μV, and the energy of the useful signal is mainly distributed between 10 Hz and 500 Hz [The amplitude of the sEMG signal collected by the electrode sensor is usually between 15 and 100 d 500 Hz , 24. At The filtered EMG signal retains the main energy part of the signal. The goal of sEMG signal pattern recognition is to identify specific actions that are in demand, and some studies have proposed active segment detection algorithms for signals . For datThe data value of the sEMG signal collected after filtering and motion segmentation is extremely small, and the difference between the data is generally 100 times, which directly affects the experimental results. Normalization algorithms such as Min-Max normalization, Z-score normalization, or conversion to a fixed range are usually used. Our experiments achieve good results on Z-Score normalization. Its mathematical formula is as follows:x1, x2, x3, …, xn independently by electrode channel:μ is the mean of the population data for a single electrode channel, σ is the standard deviation of the overall data for a single electrode channel. Z-Score normalization was performed on the 12-channel sEMG data in turn. The data of 400 sampling points with a duration of 200ms were selected for comparison.Convert all segmented sEMG motion data −4 to around 1, this process preserves the same electrode channel signal distribution, reduces the influence of outliers, and concentrates the data into more easily distinguishable intervals.As shown in After the sEMG signal is normalized, we decompose it into small window segments using a sliding window strategy and an overlapping window scheme to fully utilize the computational power of the system. To compare our proposed method with previous work, we follow the segmentation strategy in the former study , 26. ForThe fragment EMG obtained by sliding segmentation is denoted as The MSFF-Net model proposed in this paper is used for gesture recognition of EMG signals. The model structure is shown in The overall network can be divided into three stages, data input stage, multi-stream convolution stage, and global feature aggregation output stage.T (Time) can be regarded as the length of the image, E (Electrode) can be regarded as the width of the image, C (Channel) is the number of feature channels, and C = 1 means that the image is a single feature channel. The Fragment EMG after the dimension increase avoids the loss of information caused by the compression of the feature information matrix between different electrodes into vectors during the convolution process.The data entry stage for each fragment EMG is represented as The new combination of images referenced the location of the electrode acquisition: eight electrodes were equally spaced around the forearm, two electrodes were placed on the flexor digitorum and extensor digitorum superficialis, and two electrodes were placed on the biceps and triceps . We diviThe role of the multi-stream convolution stage is to extract high-level semantic features. Each branch contains batch normalized convolution modules and residual convolution attention mechanism modules. The batch normalization convolution module is mainly composed of a convolution layer, a ReLU activation layer, and a batch normalization layer. The residual convolutional attention module consists of feature channel attention, spatial attention, and residual modules.T = 400, E = 12, C = 32). The late fusion network fuses the outputs of the last layer of the multi-stream convolution, extracts the high-level semantic features of the data, and obtains the output T = 400, E = 12, C = 128). Finally, the output of the early fusion network and the late fusion network is aggregated by the feature channel Concatenation to obtain the global feature fusion network layer and the output T = 400, E = 12, C = 160).The global feature aggregation output stage consists of two sub-networks: an early fusion network and a late fusion network. As shown in Hfinal gets the output T = 400, C = 160) after passing through the global mean pooling layer.The second layer is the global mean pooling layer, which adds and averages the pixel values of each feature channel, and outputs a neuron for each channel to represent the corresponding feature map. n = number of gesture categories). This layer obtains a label vector with a length equal to the number of gesture categories through the fully connected layer, and then the Softmax function predicts the category probability distribution of the label vector. Finally, the result with the highest probability of obtaining a vote is used as the predicted category.The third layer is the fully connected layer, which re-assembles the local features into a complete graph through the weight matrix, and then adds Dropout to prevent overfitting. The last layer uses the fully connected layer of the Softmax activation function to obtain the final classification result In the multi-stream convolution stage, we adopt two batch-normalization convolutional modules (BConv). Contains convolutional layers, ReLU activation layers, and batch normalization layers (BN).C = 1) composed of multiple electrode channels can be regarded as a grayscale image of a single feature channel. In the batch normalization module, we learn the high-level semantic features hidden by the fragment EMG with 2D convolutional kernels. For the convolutional layers, we use narrow and long convolution kernels that are different from the standard size. Taking the first branch as an example, the size of the convolution kernel is 8×1, which means that each convolution obtains 8 sampling points along the T direction, and obtains the data of one adjacent electrode sampling channel along the E direction, that is, within 4ms of a single collection channel. This process separates different electrode samples, focusing on the morphological characteristics of the signal of a single electrode sample at different periods. The size of the convolution kernel is 1 × 8, which means that each convolution obtains 1 sample along the T direction, and obtains the adjacent 8 electrode sampling channel data along the C direction, that is, the sampling data of the 8 electrodes at the same time. This process separates data at different times and links different electrode sampling channels. Each convolution kernel generates a corresponding feature map.The Fragment EMG The convolutional data is activated by ReLU to make the network sparse and reduce the interdependence between parameters, which alleviates the occurrence of overfitting. After the data is processed by the BN layer, it is closer to the origin, so that the activation function in the convolution process of the next layer can obtain a larger gradient, and at the same time, the sparsely distributed data after activation is more closely linked. Data that is closely related is more likely to be fit by machine learning features.In recent years, the Attention Model has gradually become an important concept in neural networks. By imitating the idea of human visual attention, applied research has been carried out in different application fields. The convolutional block attention module (CBAM) is an improved model based on CNN and attention mechanism, including two sub-modules of channel attention structure (CAS) and spatial attention structure (SAS). It was proposed and applied to image classification in 2018 , and theThe CBAM module with residuals structure is shown in Considering the particularity of the Fragment EMG W0 and W1.We follow the approach of Woo et al.  and utilσ represents the sigmoid function, fa×a represents the convolution kernel size a × a is the convolution process.Spatial attention focuses on the location information of information on feature maps, which is complementary to feature channel attention. To compute spatial attention, we apply average pooling and max pooling operations along the feature channel axis and concatenate the two to generate efficient feature descriptors. Applying pooling operations along the feature channel axis can effectively highlight informative regions . On the a × a convolution kernel in the spatial attention part based on image processing, using a 2×1 convolution kernel to extract the morphological features of adjacent temporal signals, and a 1×2 convolution kernel is used to extract the spatial features of adjacent electrode acquisition channels. This process corresponds to the Conv2D network layer of the SAS module in We changed the size of the In this study, NinaPro databases DB2 and DB4, which contain tasks related to upper extremity movement, were used for the experiments. NinaPro is a publicly accessible database that has previously been used for myoelectric interface implementations to decode human hand movements. The DB2 sub-database collected sparse sEMG data from 40 healthy subjects including 11 females and 29 males using 12 Delsys wireless electrodes on the subject’s forearm surface and filtered through a Hampel filter to eliminate 50 Hz power frequency interference . The DB4μV), and the data collected by DB4 is at the volt level (V) after amplification.Both databases follow the same experimental acquisition protocol, and the captured gesture actions are divided into 3 exercises, the detailed actions of which are shown in It is worth mentioning that the Ninapro database has two sets of movement classification labels and corresponding movement repetition labels; the stimulus records the labels generated for each sample using the stimulus generator; the restimulus records the posterior labels of the movements. The processes associated with movement durations in the posterior labels are refined to represent real movements . It is sThe repetition is temporally synchronized with the stimulus recordings, recording the number of repetitions and the duration of each action, and can be used to do segmentation of the active segment of the signal. We experimentally did both kinds of data separately considering the differences in labeling, which is something that has been rarely mentioned by others except Rahimian et al .For dataset partitioning, we follow the dataset partitioning strategy in the former study , 26. AftDue to the small number of complete gestures in each category in the Ninapro database and the sparsity of EMG signals, overfitting is easy occurs. Furthermore, the amplitude and duration of sEMG cannot be fully replicated during motion repetition acquisition, we time-warped the training set data  and expaWe take the data of 500 samples as an example. The time-warped data randomly changes the timeline of the original data. The degree of time-warping is controlled by the number of speed changes and the ratio of the maximum/minimum speed. The warped data retains the difference between different channels. real-time and signal amplitude. We take the data of 500 samples as an example. The time-warped data randomly changes the timeline of the original data. The degree of time warping is controlled by the number of speed changes and the ratio of the maximum/minimum speed. The time-warped data preserve the synchronization and amplitude between the different electrodes.We adopted the same intrasubject schemes as those were most commonly used in existing studies on the NinaPro database , 33, 34.Classification Accuracy: Classification accuracy is defined as the ratio between the number of correctly classified gesture segments in a trial and the total number of gesture segments tested. The Accuracy (Acc) of the target object is calculated as follows:Overall classification accuracy: Overall classification accuracy  is defined as the average of the classification accuracies of all experimental individuals and is calculated as follows.The network proposed in this paper is implemented based on Keras with Tensorflow as the backend and is trained using RTX2080ti. The loss function uses the cross-entropy function, and the model is trained using the Adam optimization algorithm. The number of training sessions is set to 50 epochs, and the learning rate is set to 0.001. During the training process, the model with the highest validation set Accuracy will be saved as the final model.Our proposed method follows the dataset partition of the former study. To compare with more researchers, we use the repeated gesture cross-validation method to conduct experiments on the stimulus data and the restimulus data respectively, and the result is taken as the average of the overall recognition accuracy in multiple experiments. As shown in The results in To compare the differences between different subjects, we show the average recognition accuracy of 49 categories of actions for 40 subjects during four cross-validation processes on the DB2 database. As shown in The smaller box of the subject cross-validation results indicates that our network can generalize to the recognition of repeated gestures from the same subject. To further analyze the reasons for the discrepancy in recognition accuracy, we combine the attributes already given by the database  and takeFinally, a bivariate graph is drawn according to the subject attribute information, as shown in Our inference is that the apparent difference in gender attribute accuracy may be because females are generally inferior to males in the intensity of action stimuli. Differences in the accuracy of the laterality attribute may be influenced by minority subjects and by motor differences between left-handed and right-handed individuals.To compare the difference in the accuracy of different action recognition, we randomly show the accuracy of 49 categories of action recognition in a cross-validation experiment for 1 subject in DB2. As shown in To test the effectiveness of our proposed method in more databases, we also conducted the same experiment on 10 subjects in the DB4 database, and the cross-validation results of repeated gestures four times on the training set are shown in The cross-validation results verify the generalization ability of the proposed method on the DB4 database, and In our proposed EMG gesture recognition network, the standardization of input data, the number and size of convolution kernels in the multi-stream convolution stage, the dropout rate in the early and late aggregation stages, and the weights of early fusion and late fusion all affect the final recognition accuracy. To determine the ideal parameter settings, we selected the raw label data of 3 subjects in the DB2 database as the base data and analyzed the effects using the same experimental settings. Where S1 is male, S26 is left-handed, and S38 is female. The subject’s classification Accuracy (Acc) was used as the evaluation metric.The general batch normalized convolution process order is a convolutional layer, BN layer, and activation function . In our Analyzing the experimental results, we believe that some of the output of the features after convolution may be negative, and these features will be truncated by ReLU nonlinearity. If normalization is done before ReLU activation, including negative values in the normalization before culling from the feature space brings a suppressive effect. The activated BN will normalize the positive features and pass them to the lower convolution, without counting the unpassable features.In our proposed EMG gesture recognition network, the global feature aggregation output stage performs feature-level fusion on the outputs of the two sub-networks of early fusion and late fusion. We obtained the results after multiple experiments as shown in To better analyze the source of our results, we refer to the method of ablation experiments  to analyIt can be seen from The multi-stream feature fusion network proposed in this paper uses multi-stream convolution and spatial attention, and according to the characteristics of multi-channel EMG signals, adaptively extracts the morphological features of the signal of a single electrode acquisition channel and the spatial features of multiple electrode acquisition channels at the same time. The feature channel attention mechanism is used to adaptively assign different weights to different feature maps, increase the weight of effective feature maps, suppress invalid feature maps, and obtain more detailed features. Finally, an aggregation network is used to aggregate early primitive features and late high-level semantic features to output classification results. The average recognition accuracy of 49 types of gestures on the stimulus label data reaches 87.02%, which brings optimization from the classification performance and data requirements.We compare the recognition model in this paper with gesture recognition models that have been studied on the Ninapro database in recent years in Among them, Hu , Ding 3, Gulati Compared to these studies using the Ninapro database, our network does not need a professional artificial feature set design, directly uses the original EMG signal as input, and uses four-time cross-validation of repeated gestures on the training set to make the results more reliable. But nearly a quarter of the real data in the cross-validation process is not directly involved in network training, so we enrich the training set with time-warped data augmentation. We excluded rest states in the number of classified gestures because most of the existing studies in the Ninapro database , 35–37 eIn these studies using other databases, Compared to , the inpIn this paper, a novel MSFF-Net sEMG signal gesture recognition model is proposed. The biggest feature of this model is that the signals collected in different areas are analyzed separately according to the electrode position, which can more fully extract the features of the multi-channel sEMG signal. In the signal preprocessing stage of this method, the denoised sEMG signal is segmented to ensure the independence of each action, and then the order of magnitude of the signal is changed by Z-score standardization to facilitate feature extraction. Finally, sliding segmentation is performed on the action segment to identify the signal from the perspective of the image. In the feature extraction stage, the multi-stream convolution network isolates the signal interference of different muscle regions and retains the signal correlation of the same muscle region. ResCBAM module further extracts deep features from signal shape, electrode acquisition space, and feature space, and the early-late aggregation network integrates the original features after signal standardization and the high-level semantic features of multi-stream convolution. Experiments show that the average recognition accuracy of the proposed model for 49 types of gestures of 40 healthy subjects in NinaPro’s DB2 database and 52 types of gestures of 10 healthy subjects in the DB4 database is better than the existing similar methods. The proposed network model helps to improve the accuracy of gesture recognition based on sEMG and provides a new idea for the current research of human-computer interaction based on sEMG. In the process of motion acquisition of sEMG signal, the phenomenon of sensor dislocation or movement is inevitable, which may affect the performance of the reference electrode arrangement classification method proposed in this paper. In the follow-up research, we will further try to solve this problem by combining feature set images and signal images.


Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm. Submarines are ships that can operate independently underwater. Since its appearance, it has been widely used in various military operations and has become an important part of modern naval operations.With the continuous changes in the international situation, a series of studies on underwater navigation of submarines have received extensive attention. Different to surface ships, submarines face more unknowns and threats underwater, all of which pose challenges to the safe underwater navigation of submarines. In order to ensure the safety of navigation, submarines need to find the path that best meets the mission requirements among the many optional routes. Therefore, a suitable path planning algorithm is particularly important.Due to the complexity and nonlinearity of the underwater 3D environment, the 2D land-based algorithms cannot be directly used to solve the underwater path planning problems.In response to this issue, many scholars have focused on the development of underwater path planning algorithms. At present, they can be categorized into graph search-based approaches, such as Dijsktra algorithm ,2,3, A* lgorithm ,5; sampllgorithm ,7, RRT* lgorithm ,9; artiflgorithm ,11; evollgorithm , particllgorithm ,14, genelgorithm ,16, difflgorithm ; heuristlgorithm ,19, simulgorithm ,21.Based on these algorithms, a large number of scholars have developed path planning algorithms adapted to the three-dimensional environment or the marine environment.Arinaga et al. applied Dijkstra algorithm to a global path search for AUVs in an underwater environment. The algorithm can avoid a set of obstacles and reach the end but does not consider the impact on the marine environment . Garau e factors . Carrerasfactory . Jantaprsfactory . Liu et sfactory . Ma et asfactory . Rafael sfactory . It is worth mentioning that, with the gradual complexity of application scenarios, the combination of multiple algorithms has become a hot research direction in the field of path planning.Zhang et al. proposed a branch selection rapid exploration random tree (BS-RRT) algorithm to solve the global path planning problem in the narrow channel environment of UAVs. However, these two algorithms are aimed at aerial unmanned equipment, which is not the same as the background of this study . Yu et aironment . The algAt present, due to the special mission background, there are few studies on the path planning of submarines. More researchers have turned to the path planning algorithms for underwater vehicles such as AUVs. Although the operating environment of AUVs is similar to that of submarines, there are significant differences between the two in terms of size, mission background, and dynamic characteristics. These differences are summarized in In this paper, the APF-ACO algorithm is proposed to solve the underwater global path planning problem of submarines. The algorithm is able to converge rapidly in the underwater environment. Moreover, the path planning results obtained by this algorithm are more advantageous and more stable.In order to further optimize the path planning results obtained by the APF-ACO algorithm and make it more in line with the navigation requirements of submarines, this paper develops an inflection point optimization algorithm and a path smoothing algorithm. Experimental results show that the algorithm significantly reduces the path length and the number of inflection points.A dynamic obstacle avoidance algorithm based on the velocity obstacle method is proposed, which further improves the content of the submarine path planning algorithm. The experimental results show that the dynamic obstacle avoidance algorithm can accurately identify and avoid threatening dynamic obstacles.Discuss the real performance of the algorithm in the submarine semi-physical simulation system. From the results, the real feasibility of the algorithm under the underwater navigation of the submarine is verified.Considering the applicability and stability of the algorithm, we based on the relatively mature ant colony algorithm, combined the artificial potential field algorithm, and proposed a new artificial potential field ant colony algorithm (APF-ACO). The algorithm is not a simple combination of several algorithms, but a special development for the characteristics of submarine underwater navigation. The algorithm can effectively deal with the underwater path planning problem of submarines. In this sense, the main contributions of this paper include:In this section, we discuss the physical background and derivation of the algorithm in detail.The environment model simulates the real application environment with an abstract physical and mathematical model. The representation of the model directly affects the efficiency and reliability of the path planning algorithm.z-axis value of 0. Vertical down along the depth is the positive direction of the z-axis. The direction of increase along latitude is the positive x-axis. The increasing direction along the longitude is the positive y direction.In this paper, the path planning environment is constructed by the method of spatial equal mesh. As shown in x-axis. Each plane is then divided into y- and z-axes. In this way, the planning space is divided into In this way, we constructed a cuboid underwater planning space. Let’s say we divide space into In the planning space, each point in the subspace can be spatially located with coordinates The original artificial potential field function is prone to deadlock when there are a lot of obstacles around the target point, which makes the algorithm unable to effectively converge. To solve this problem, the repulsive function of the artificial potential field algorithm is improved in this paper. We introduce the distance between node and target point to adjust the size of the repulsive force field to prevent the planning from falling into local deadlock. The improved repulsion function (1) is shown.By calculating the negative gradient of The expression for gravitational potential energy is:In this way, the resultant force of the artificial potential field can be obtained:The heuristic function of the traditional ant colony algorithm only considers the distance between the current position and the next node. When there are a large number of obstacles near the endpoint, the algorithm tends to fall into a local optimum. Not only that, but the positive feedback of the ant colony algorithm may also make the final planned path, and not the global optimal path.Therefore, this paper introduces the resultant force The basic ant colony system local pheromone update rule isThis formula adopts the uniform update rule and does not consider the influence of pheromone diffusion on pheromone distribution, which deviates from the real ant colony system. In order to restore the efficiency of the ant colony system and improve the convergence speed of the original ant colony system algorithm, this paper introduces the pheromone diffusion model to improve the utilization efficiency of pheromone by the ant colony.We assume that the pheromone concentration follows a Gaussian distribution and that the pheromone at the current point only spreads to the adjacent forward direction grid. The simplified pheromone diffusion model is a circumscribed sphere, as shown in Where According to the above-improved algorithm, the state transition rule of ants from point Through the above steps, the algorithm not only increases the probability of ants choosing After one iteration is completed, the global pheromone update is performed, and the update rule is as follows.The specific flow chart of the APF-ACO algorithm proposed in this paper is shown in The results obtained by the path planning algorithm usually have shortcomings such as many inflection points and insufficient smoothness. To solve these problems, the path results need to be optimized. In this paper, the inflection points optimization algorithm and the path smoothing algorithm are used to obtain the path results that are more suitable for submarine navigation.In addition to global static path planning, submarines also need to have dynamic obstacle avoidance capabilities to deal with sudden threats. This paper proposes a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method to improve the survivability of submarines.First, find the inflection points in the path that the algorithm gets. Suppose there are All inflection points of the path can be obtained in this way. Add the start and endpoints, assuming there are Submarines should try to avoid large steering angles when sailing underwater. In order to meet the actual navigation requirements of submarines, this paper proposes a path smoothing algorithm adapted to the APF-ACO algorithm. The simple single-stage polynomial optimization cannot adapt to the complex underwater environment, and the complex high-order polynomial optimization is not efficient, so this paper chooses to use the Clothoid curve fitting algorithm to optimize the smoothness of the path. The Clothoid curve is based on the Fresnel integral, and the change in curvature of the curve is proportional to the arc length of the curve. The three-dimensional Clothoid curve equations are shown in (14) and (15). In this paper, two-dimensional Clothoid curve fitting is performed on Suppose the path point obtained by the APF-ACO algorithm is This formula determines that the tangent angle After initially completing the path smoothness optimization, it is also necessary to consider that the optimized path may collide with obstacles again. Therefore, the smooth path must be re-examined. As shown in The flowchart of the smoothing optimization algorithm is shown in The velocity obstacle method is an algorithm that uses geometric constraints to express methods to avoid collisions with obstacles. Its schematic diagram is shown in We inflate the submarine into a sphere of radius When We assume that the velocity vector of the carrier and the obstacle do not change during the calculation process, then define a ray from the center of the carrier along the relative velocity direction:The relative velocities with collision risk are grouped together, which constitutes the “collision domain” of dynamic obstacle avoidance. The mathematical description of the “collision domain” is:The velocity obstacle method uses a definite value when describing the dynamic obstacle, but there is a certain inevitable error between the sensor accuracy and the submarine’s own speed, so the submarine may accidentally collide with the obstacle. In order to solve this hidden danger, this paper decided to introduce the parameter of “safe distance”. Compared with the size of the submarine itself and the size of the obstacles, the marine navigation environment is very broad, so the parameter of “safety distance” has practical application value and feasibility. The “safety distance” radius is given by empirical values of sensor accuracy error and submarine speed error.The corrected obstacle area radius is:In addition, in order to enhance the adaptability of the algorithm, this paper supplements the dynamic obstacle avoidance algorithm. After dynamic obstacle avoidance, if the current node cannot safely reach the next node of the original path planning result, the current node is used as the starting point for re-path planning. Repeat this operation until the submarine reaches the end. The flow chart of the dynamic obstacle avoidance algorithm is shown in In this paper, a 10 × 10 × 10 (excluding boundary) three-dimensional space environment is constructed for simulation experiments, in which spheres represent obstacles. In order to verify the effectiveness of the APF-ACO algorithm proposed in this paper, relevant experiments are designed in this section.In order to verify the effectiveness of the APF-ACO algorithm, the experiments designed in this paper are compared with the other three algorithms in various obstacle environments. Among them, the Optimized ACO algorithm is an underwater ant colony optimization algorithm proposed by the reference .In this paper, experiments were designed in five obstacle environments, and each experiment was carried out independently 10 times, and statistical significance tests were performed to ensure the reliability of the results. The parameter settings of the algorithm are shown in The experimental results are shown in In order to verify the effectiveness of the APF-ACO algorithm proposed in this paper, we further compare the computational time cost of four different algorithms under the same conditions, and the calculation results are shown in It can be seen from the experimental results that the calculation time of The Original APF algorithm is the shortest, followed by the calculation time of The Original ACS. The computation time of the APF-ACO algorithm is almost the same as that of the Optimized ACO algorithm, which is about 10% longer than that of The Original ACS. The results show that the two optimization algorithms sacrifice about 10% of the computing time to obtain better path planning results, which have practical application value.From the above experimental results, it can be seen that the APF-ACO algorithm proposed in this paper and the Optimized ACO algorithm have significant advantages. In order to further compare the performance of the two algorithms, this paper designs experiments to compare the number of path inflection points and the convergence speed of the operation. In five different experimental environments, the visual planning results of the two algorithms are shown in The establishment of five experimental environments mainly considers the influence of different obstacle distribution patterns on the calculation results of the path planning algorithm. The distribution of obstacles in test environments 1 and 2 is more discrete, the distribution of obstacles in test environment 3 is more concentrated, the distribution density of obstacles near the endpoint is increased in test environment 4, and the distribution density of obstacles near the start point is increased in test environment 5. It can be seen from the path planning results that in the above five test environments, both algorithms can obtain complete and feasible paths. However, it can also be clearly seen that the path obtained by the Optimized ACO algorithm is more tortuous when the obstacles are dense. In order to further compare the pros and cons of the two algorithms, we recorded the average number of inflection points of the planning results of the two algorithms in each experimental environment, and the results are shown in It can be seen from the results that the path obtained by APF-ACO is straighter and the number of inflection points is reduced by about 18.8%. This is mainly because the APF-ACO algorithm introduces the potential field force parameter so that it can consider the distribution information of obstacles in the operation of each step. This allows the algorithm to avoid obstacles as early as possible. This feature of APF-ACO also fits the real needs of submarines sailing underwater.Convergence speed is an important indicator to measure the performance of an algorithm. This paper next compares the convergence speed of APF-ACO and Optimized ACO. We conducted 10 independent experiments in experimental environment 3 and averaged the convergence rate results, and the obtained results are shown in As can be seen from the figure, the convergence speed of APF-ACO is about 47.7% faster than that of Optimized ACO. At the beginning of the iteration, APF-ACO can obtain better initial path results, mainly because the addition of the potential field force parameter can increase the cost difference between the path points to be selected, thereby greatly increasing the probability of selecting a better path. In the later stage of iteration, the convergence result of APF-ACO is significantly better than that of Optimized ACO, mainly because the introduction of the potential field force parameter can always guide the path to find the direction close to the endpoint, avoiding the convergence result falling into the local optimum.In summary, it can be seen that the APF-ACO algorithm has certain advantages in terms of convergence results and convergence speed and can adapt to the complex underwater path planning application scenarios of submarines.The simulation experiment of the path smoothing optimization algorithm is carried out in the experimental environment 5. The inflection points of the path are optimized before smooth optimization, and only the necessary inflection points of the path are retained. The final result is shown in The green dotted line in The purple solid line in In order to verify the dynamic obstacle avoidance effect of the speed obstacle method, this paper constructs a dynamic obstacle environment for experiments. The experiment introduces dynamic obstacles on the basis of global path planning to verify the effectiveness of the dynamic obstacle avoidance algorithm and its compatibility with the APF-ACO algorithm. The visualization results are shown in We construct three dynamic obstacle environments to verify the reliability of the dynamic obstacle avoidance algorithm. We show two different perspectives of each path planning result. As shown in It can be seen from the experimental results that the dynamic obstacle avoidance algorithm proposed in this paper can achieve the expected effect and has a practical application value.In order to further verify the application of the algorithm proposed in this paper in the real environment, we applied APF-ACO to a semi-physical simulation software specially designed for the study of submarine underwater path planning for experiments. The system uses the 0.25° submarine topography data provided by GEBCO  to generate virtual submarine topography to simulate the real underwater navigation environment of submarines. In addition, the system adopts the standard SUBOFF full-body submarine model and fully considers various maneuvering rules that submarines have underwater, such as maximum pitch angle, maximum diving depth, limit turning radius, etc.The path planning range selected for this system test is The specific experimental results are shown in It can be seen from the experimental results that the submarine can successfully avoid terrain obstacles and reach its destination. The result of the path is relatively straight as a whole, and it can actively maintain the navigation at the same depth, which is in line with the underwater maneuvering characteristics of submarines. The results verify the effectiveness of the APF-ACO algorithm as a submarine underwater path planning algorithm in the real marine environment.In this paper, a path planning algorithm suitable for submarine underwater navigation is proposed. This algorithm is a composite method that includes global path generation and local path adjustment. In the global path planning, we presented an improved Artificial Potential Field Ant Colony Optimization (APF-ACO) algorithm to adapt to the underwater path planning needs of submarines. The experimental results showed that APF-ACO can stably obtain path planning results in a variety of experimental environments. Compared with the Optimized ACO, APF-ACO can obtain path results with shorter length, fewer inflection points, and better stability. Not only that, but APF-ACO also has a faster convergence speed, which meets the tactical needs of submarines.We also propose an inflection point optimization algorithm and a smooth optimization algorithm to further improve the path obtained by APF-ACO, making it more in line with the actual underwater navigation of submarines. The experimental results show that the inflection point optimization algorithm can effectively reduce the unnecessary inflection points of the path and greatly reduce the length of the path. The smoothing optimization algorithm can increase the smoothness of the path without intersecting with obstacles.In order to make the algorithm more complete, we have added a dynamic obstacle avoidance algorithm based on the motion obstacle method. The introduction of this algorithm can help submarines identify threatening moving obstacles and re-plan local paths. The experimental results show that the dynamic obstacle avoidance algorithm can successfully help submarines to identify and avoid dangerous dynamic obstacles.We use a professional semi-physical simulation system for scene verification of the APF-ACO algorithm. In the system, we use real seabed topography data and real starting and ending position data to conduct experiments. The experimental results show that the APF-ACO algorithm can be effectively applied in the navigation tasks of submarines and has practical application value.We insist on affirming the application value of path planning in submarine navigation in the new era. In future research, we will further optimize the submarine’s path planning algorithm. We consider introducing the influence of marine environmental elements on submarine navigation into the path planning algorithm to expand the scope of application of the algorithm. Next, we seek to apply the algorithm to real submarine navigation tasks and collect data in real experiments to further improve the algorithm.


Using a large sample of enterprises from a survey that was simultaneously conducted in Germany, Austria and Switzerland, we study the self-reported impacts of the adoption of “green” energy saving and related technologies (GETs). Our specific interest is in how different policy instruments associate with energy efficiency, the reduction of  Sustainable Development Goals (SDGs), among which SDG8 occupies a central position for simultaneously promoting sustainable and inclusive economic growth and employment. Among others, Stern and Stiglitz  launched its Agenda for Sustainable Development with an aim to pave the way for “prosperity and opportunity for all on a healthy planet.”Stiglitz  highlighStiglitz , 397 poiStiglitz , p. 19 aThis paper investigates how public policies associate with the adoption of new green energy technologies (GETs) and the self-reported impacts on competitiveness in a large sample of individual companies. Within the far-reaching agenda of the SDGs, it aims to contribute in particular at the intersection of energy efficiency, technological change and climate change.But do we really need policy to interfere with the energy efficiency of private businesses? From a conventional economics perspective, there are valid reasons to answer in the negative. Higher efficiency implies lower cost to the individual enterprise, thus providing private incentives to adopt new GETs as long as the savings match the cost of investment. Also for most non-renewable energy sources property rights are well established. If consumption is excludable, prices reflect rents from scarcity and the rate of exhaustion should be welfare-efficient .The focus of our research is on such technological change. But unlike the growing literature on own environmental innovations, which uses patents, R&D expenditures or other relatively well-available measures from common innovation surveys as dependent variables, we are interested in the There are obvious similarities in how the three countries deploy the policy instruments mentioned above. For instance, in the field of energy policy guaranteed feed-in tariffs for electricity from renewable sources exist in the entire region. They intend to foster investments in the production of renewable energy by reducing the uncertainty about future demand and prices, but also imply that final users must bear higher electricity prices. Similarly, all three countries levy various carbon taxes, e.g. on fossil fuels, and participate in trading systems for carbon emissions. Finally, in innovation policy the growing concerns about climate change tend to shift funding priorities in favour of GETs.Germany has put much emphasis on the promotion of renewable energy,But each country has its distinct history, institutional setting and policy framework. For example, during the past decades Austria the relative abundance of hydraulic power has provided a comparative advantage to energy intensive industries, such as metal processing or cellulose and paper, which still produce a comparatively high shares of manufacturing output. Another particular characteristic is Austria’s ban on nuclear energy, which was enacted after a public referendum in 1979. Energy policy thus always ranked relatively high on the public agenda, while energy intensive producers enjoy considerable political leverage.Energy Efficiency Law became the focal point of the country’s regulatory approach in this area. After adopting EU guidelines, energy suppliers  must prove concrete measures to achieve annual efficiency gains of 0.6% relative to their previous year’s total energy sales. These efficiency gains may originate either from their own operations or from their customers and depend on the ratio of energy inputs to output .In Swiss policy has generally been more reluctant to intervene in favour of GETs. The Swiss framework is mainly characterized by market incentives and voluntary agreements.Compared to Germany and Austria, After these introductory remarks, Sect. As stated in the introduction, the core question of this research is how environmental policies relate to the perceived impacts on the competitiveness, energy efficiency and positive statements, which refer to the importance of actual policies and impacts of the firms in our sample, i.e. as observed for the DACH region during that period. A low or insignificant coefficient of any instrument may thus be due either to the insufficient scope of (an otherwise effective) intervention, its inefficient implementation, or a bad choice of policies. Similarly, significant coefficients can only indicate that a certain policy appears in principle to be effective, demonstrating a positive statistical association for our sample of firms in the DACH region.The comprehensive nature of the enterprise survey : Policy x associates positively with the firms propensity to adopt new GETs. It must be rejected, if the policy either shows no significant coefficient or associates with a significant decrease in the probability of adopting new GETs.Intensive margin (H2): Policy x associates with a higher share of expenditures for new GETs in total investments. It must be rejected, if the policy associates with a decrease or does not significantly relate to the intensive margin of adoption.Heterogenous effects of different policy instruments (H3): The statistical association of different policies varies according to particular aspects of adoption, such as the primary motivation, the introduction of EMS, extensive and intensive margins, or different areas of technology.The latter hypothesis is explorative, since little is known about it from the literature. The theoretic model and numeric simulations of Acemoglu et al. : The adoption of GETs tends to increase the self-reported energy efficiency of firms. It must be rejected, if adoption has a significant negative association or does not significantly relate to energy efficiency.Perceived impact onemissions (H5): The adoption of GETs tends to reduce the self-reported Turning to the self-reported impacts on competitiveness, contemporary concepts at the aggregate level emphasize the positive contribution of cleaner production to a society’s overall standards of living. However, these social benefits are largely external to the individual firm, which bears the private cost of abatement and compliance to environmental regulations : The adoption of new GETs associates with a self-reported decrease in the competitiveness of firms. It must be rejected if it associates significantly with its increase or does not significantly relate to it.In contrast, generalising the insights from a rich repository of case studies, Porter (17Porter hypothesis (H6b): The introduction of new GETs associates with a self-reported increase in the competitiveness of firms. It must be rejected if it associates significantly with its decrease or does not significantly relate to it.The Porter hypothesis has triggered much controversy and has provided a fruitful platform for further research. It has offered stronger theoretical explanationsA further two hypotheses address the self-reported  Pasurka .16 The i Pasurka , which i, Porter  and Port, Porter  argue toNeutrality hypothesis (H6c): The adoption of new GETs does not significantly associate with the competitiveness of the average enterprise in a cross-section of firms. It must be rejected if it significantly associates with either an increase or decrease of the self-reported competitiveness of the average firm.One likely explanation is that environmental regulations apply uniformly to a given firm population, whereas the induced innovation races tend to produce skewed returns Popp . TypicalThe data used in the analysis originate from a comprehensive enterprise survey conducted in Germany, Austria and Switzerland. The survey focused on the creation and adoption of new energy saving and related technologies. It was jointly developed and simultaneously launched in the summer of 2015 by the ETH Zürich, the Center of European Studies (ZEW) and the Austrian Institute of Economic Research (WIFO). In total, the gross sample amounted to 19,254 firms. The firm samples are comprised of manufacturing, construction and services, except for the energy sector and state-related services, such as public administration, education or health. They were built from a stratified random sample, with stratification applied to two-digit industries and three industry-specific classes of firm size but full coverage of large firms. Comprehensive recall actions in all three countries ensured a sufficient number of respondents covering all industries and size classes according to the sampling scheme. Only for Austria some cells could not be filled separately and were therefore merged with a neighbouring cell. The net sample of valid responses includes 4,634 firms, 49% of them in Germany, 39% in Switzerland and 12% in Austria.The questionnaire asked about basic firm characteristics, general energy-related characteristics as well as inducement factors and barriers to the adoption or own innovation of GETs. Innovation related questions, however, were only directed at a subgroup of manufacturing sectors . Consequently, the number of observations considerably declines for all estimations where own innovation is included among the regressors.The identification of the relevance of different energy policies at the firm level is hardly possible through data from public sources. We hence asked managers to rate the relevance to their business on a three-point Likert scale.Developing our own survey also allowed us to reduce the risk of a common response bias. Avoiding a frequent problem of enterprise surveys, we deliberately asked all firms about the relevance of the various policy related factors, irrespective of whether they had actually adopted new GETs or not. Thus we can also establish the relevance of the inducement factors when policies target different firms, originate from multiple territorial levels, or are subject to imperfect monitoring and enforcement.20determinants, we distinguish between general firm characteristics (Firm), specific energy-related factors (Enr), inducement factors (Idc), barriers to adoption (Bar), fixed effects for the industry (Ind) and the country (Ctr) in which the responding firm i is located. Tables The variables are organised along the three dimensions of (i) determinants, (ii) activities and (iii) impacts. Among the independent Among the inducement factors, we distinguish five types: (a) energy-related taxes and duties; (b) subsidies for developing and adopting green energy technologies; (c) standards and negotiated agreements; (d) other regulations on energy use, such as emissions caps and certificates,extensive margin of adoption by technology field. In contrast, we measure the overall intensive margin by the share of total expenditures on the adoption of any GETs in total investments of the firm  energy efficiency, (ii) the reduction of n equations. On the right side, the first vector gives the constant intercept k common independent variables, depicted by the coefficients probit regressions, which apply the maximum likelihood principle to cumulative normal distributions. The coefficients tell the impact of the independent variables on the respective response probabilities. Analogously, we apply an ordered probit regression to fit ordinary least squares (OLS). Alternative methods  are used to test the robustness of the empirical findings.For the econometric specification, we again start with the impact of policy on adoption. On the left side, we find the dependent variables for each of the multivariate probit model, which applies the method of simulated maximum likelihood (SML) to jointly fit the five different binary choices of adoption covered by the survey:25k.For the individual technology fields, we use a mentclass2pt{minimordered probit regressions with the above adoption choices entering as explanatory variables:t denotes the adoption variables used in the impact equations. The superscript u denotes the general control variables. Their number must again be equal to the number of variables referred to in the superscripts on the right side of the following expression:Turning to the impacts of adoption, we are interested in three dependent variables: mentclasspt{minimaFinally, we want to emphasise that economics is often considered a science of ‘universal interdependence’. Endogenous relationships abound and the econometric identification of strict causal impacts can be extremely demanding. Ideally, we would therefore like to estimate the above relationships simultaneously by means of a structural equation model.Despite this limitation, we aim to track the presumed causal structure as closely as possible and to disentangle mutual impacts of our target variables by means of the vast array of single equations as summarised in Fig. average marginal effect (AME) on the respective probabilities of adoption for those factors, which are statistically significant. Figure The following tables and figures summarise the results from the various econometric estimations. To begin with, Table energy-related management systems we test how the inducement factors associate with their adoption as well as the further correlation of EMS with the adoption of GETs. In short, firms which are large, part of an enterprise group, or exporters have a higher probability of adopting EMS. In addition, environmental taxes and standards are significant policy-related factors that raise their probability of use by 6.1 and 5.9 percentage points (pp), respectively. Their introduction increases the firm’s probability of adopting new GETs, with the AME amounting to 15.7 pp overall. It is significant in each of the technological fields, but strongest in buildings, followed by ICT, production, transport and renewable energy sources. These results are thus consistent with the studies by Khanna et al.  relevant to their business, is 7.6 pp. It is strongest for ICT related GETs, followed by GETs in transport, production, renewable energy and buildings. Overall, this finding is consistent, for instance, with a recent study by Aghion et al. . This result is largely consistent, for instance, with the findings of Lanoie et al.  only affect this intensive margin of adoption. Public funding does so consistently if we either include or leave out own innovations in GETs from the set of control variables. In contrast, the findings on regulations (other than standards) are inconclusive as their impact is negative in one specification, but becomes insignificant if we include own innovations among the explanatory variables in the adoption of GETs.29While neither of the above instruments appears to have a significant impact on the share of GETs in total investments, Overall, the heterogenous picture which emerges from our estimations on the adoption equations is consistent with the received empirical literature. For instance, the survey on the diffusion of green technologies by Allen et al.  conclude30lack of finance is perceived to be a significant impediment to adoption. This applies to all technology fields, except ICT.energy prices significantly raise the probability of GET adoption only in production and transport, but generally associate with a higher intensive margin. Fears of energy shortages only affect the extensive margin in the field of ICT. Rather than acting as substitutes, effective changes in the energy mix of the firm complement the adoption of EMS and new GETs.Turning to the potential barriers to the introduction of new GETs, the innovation in the field of energy-related technologies to the set of regressors reduces the sample by more than one half. We therefore display the outcome in two separate columns of Table Adding the variable on own group membership relates to a lower intensive margin of GETs. Firm size by number of employees generally associates with a higher extensive margin (except for renewable energy). And so does age (except if applied to ICTs). In comparison to German enterprises, Austrian firms more often report the adoption of EMS and GETs (except for ICT) and also exhibit a higher share of GETs in total investment. Compared to German firms, Swiss enterprises show a higher probability of introducing EMS, but a lower extensive margin and a higher intensive margin of adopting GETs. But when comparing the AMEs in Table Among other firm characteristics, genuine objective of adoption. On the one hand, firms that have introduced an EMS show a significantly higher propensity to adopt new GETs with the primary objective of raising energy efficiency or reducing The second set of equations is directed at the perceived impacts of adopting EMS and GETs on energy efficiency, production associate significantly with higher energy efficiency and reduced The self-reported ecological impacts of adopting new GETs differ considerably by technological fields. For example, new GETs in transport the adoption of GETs is perceived to have significantly reduced the firm’s buildings significantly improves energy efficiency, but not the In Turning to the impact of new GETs on the firm’s competitiveness, Cohen and Tubb  report c32While these findings from the literature on firm-level innovations may lead one to expect a negative sign, our findings support the hypothesis of a largely neutral effect of the adoption of new GETs on the competitiveness of the average firm. This result points towards the general fact that the need and incentives for adoption apply similarly to firms in the same market, leaving little scope for differential impacts on their relative competitive position. Furthermore, the potential surplus of early adoption would only apply to a few firms and not significantly affect the average enterprise in the sample. Overall, our results are thereby consistent with Van Leeuwen and Mohnen , who repFinally, among the auxiliary factors, group membership and more intense competition appear to reduce the economically feasible options in adopting GETs, while significantly decreasing their impact on energy efficiency. Using enterprise surveys one must, however, generally stay alert to the subjective nature of the data. For example, if respondents expect that their self-reported assessments may influence future policies, the relevance of more restrictive and disliked policies may be biased downwards in comparison to weaker interventions. Conversely, the assessment of policies with a direct pecuniary benefit to the firm may be biased upwards. The low and insignificant impact of subsidies on the extensive margin of adoption may, however, indicate that the particular design of the survey helped to mitigate such a bias  appear to be among the most consistent drivers of adoption. These findings point at the high practical relevance of detailed technical rules.taxes and duties show a significant association only in the field of production and with the adoption of EMS. Their limited impact, however, may also indicate insufficient use of incentive-based tools in the DACH region rather than a lack of effectiveness per se.Energy-related public funding shows no significant association with the extensive margin of adoption. This may indicate that in practice most funding institutions neither have the information to identify, which firms are at the margin of adoption, nor the means to restrict their support accordingly.In our sample, intensive margin suggests a positive role of public funding in the adoption of GETs. One likely reason is that it affects choices within a company in favour of activities that earn the subsidy and thereby help to increase their budgets relative to others.Still, the significant coefficient on the  Comparing the three countries in the DACH region, Austrian companies report the highest adoption rate for GETs and EMS. German companies are second in terms of the extensive margin of GET adoption, but behind companies from Switzerland in terms of the intensive margin of GET adoption.Policy impacts. Public debates typically center on two opposing hypotheses. One says that the conversion to green energy would harm competitiveness because it engenders additional costs. Another view, known as the Porter hypothesis, maintains that companies that implement stricter regulations and stricter environmental rules faster will be rewarded by a competitive edge. Essentially, we found that success lies primarily on the ecological side: energy efficiency and reduces According to our survey data the overall adoption of GETs significantly improves The average marginal effect (AME) of GET adoption on energy efficiency tends to be highest in Switzerland, followed by Austria, while Germany ranks third. For the AME on competitiveness was neither measurably impaired nor enhanced by the introduction of new GETs. Contrary to the aforementioned hypotheses, the overall effect is neutral. This result strengthens the case for public policies that promote the needed energy transition without necessarily compromising the United Nations’ goals for growth and employment.Finally, the firms’ Several reasons can explain the latter result in support of the neutrality hypothesis. To begin with, energy cost is a substantial but not major share of total costs in most of the companies. Moreover, energy efficiency also leads to cost savings for the respective companies. Finally, many firms, especially in the services sector, operate and compete locally. In that case, for instance, an increase in the price of electricity affects all competing firms in the same way, meaning that none of them suffers a differential disadvantage for comparable levels of energy intensity in production.Time is running short and the United Nations  has urgeAs a general caveat, however, it must also be stressed that the econometric estimates are strictly for the average firm in our sample, which covers many different industries. Therefore, there may well be competitive disadvantages for companies in energy-intensive sectors such as the steel industry or the transport sector, wherever the respective companies are additionally exposed to strong international competition. While the costs caused by the development and introduction of new GETs are often cushioned by public funding, many environmental regulations provide exemptions for energy intensive companies to deliberately cater for this situation. Such measures highlight the persisting trade-offs between environmental goals and competitiveness concerns. Their resolution will require additional instruments, such as the introduction of carbon border adjustments (CBAs) in order to reconcile the goals of curbing carbon leakage with those of a comprehensive pricing of For the average firm, however, our analysis has shown that environmental policies significantly relate to higher energy efficiency and an improved carbon footprint, without having a significant self-reported negative impact on its competitiveness. Moreover, the analysis has shown how different instruments capitalize on distinct strengths and opportunities, demonstrating the need for a comprehensive policy mix that aims to induce the adoption of GETs directly by means of technical standards, energy related taxes or subsidies, and more indirectly, for instance, by targeting demand or the implementation of EMS.


Macaca thibetana) formed a strong social bond, and there were two mechanisms  to promote the formation of coalitions. This study provides a new perspective for understanding the maintenance mechanism of social stability in non-human primates under competitive environments.The social behavior and the evolution of non-human primates have always been a focus of animal behavior. In this study, we determined that male Tibetan macaques (Macaca thibetana) in Anhui, China, and recorded the whole process of male macaques forming coalitions once agonistic support occurred. The results showed that a higher intensity of the social bond between males was associated with more frequent coalitions. Dominance rank also significantly influenced male coalitions, showing that high-ranking individuals allied more frequently. Moreover, males with longer residence times formed more stable coalitions. We suggest that male Tibetan macaques form a stable social relationship, where social bond and social rank both promote the formation of a coalition. This study provided some insights into the mechanism by which social cooperation develops in multi-male and multi-female groups.Previous studies have shown that fierce competition could promote male coalitions. There are two ways for males to choose their allies in the conflict. The first is that supporters choose high-ranking individuals, and the second is that supporters choose affiliated individuals. It is necessary to clarify the factors for forming a coalition and the process of cooperation in primates with complex relationships and strict hierarchies. Thus, we conducted a study on a group of free-ranging Tibetan macaques ( Mungos mungo) are often more aggressive towards intruders or out-groups when food is scarce [Telmatochromis temporalis) of the larger ecomorph outcompete adults of the smaller, compelling the smaller snappers to use poor shell habitat [Macaca fascicularis) may express a preference toward highly valuable females by investing more in aggression and vigilance  while mate-guarding these females [Group living in animals developed due to the long-term adaptation to the environment. Compared to a solitary lifestyle, living in groups allows animals to perform various activities, which can greatly enhance information sharing to perform social activities more efficiently, such as collective foraging and avoiding predators . Howevers scarce . Competi habitat . Among s habitat . For exa females .M. assamensis), males may utilize all-up coalitions  to attain food resources and higher social rank [M. sylvanus) choose their allies according to affiliative interactions to reduce the risk of defection in coalitions [Pan troglodytes), adolescent males form coalitions more often than other members of their group, and thus, have an advantage when competing for ranks as adults [M. fuscata), outgroup males are more likely to engage in social grooming with their partners after immigrating into a new group [Fierce male-male competition can drive males to form a firm coalition . Males uial rank . In otheial rank . A sociaial rank . Barbaryalitions . In addis adults . Namely,ew group . We beliIn primates, coalitions can be formed in two ways: (1) Coalitions are formed by supporting high-ranking individuals during conflicts . (2) CoaM. thibetana) live in hierarchical social groups. To maintain group benefits and their dominant ranks, male Tibetan macaques need to establish cooperative relationships and form coalitions during particular periods  [Tibetan macaques ( season) . Previou season) . To answ season) , males w season) , high-ra season) ,19. (4)  season) .We studied a habituated group of Tibetan macaques  inhabiting the “Valley of the Wild Monkeys”  at Huangshan, China, at an altitude of 600~1200 m. Due to continuous observations and studies on this group since 1986, all individuals in the group could be accurately identified based on their natural characteristics, and the maternal relationship of all individuals could be determined. In this study, adult and adolescent males from group YA1  were selected as the subjects.During the study period (November 2021 to January 2022), all behavioral data were collected from the group from 08:00 to 17:00. We used the focal animal sampling method and continuous recording method to collect behavioral data, including proximity (5 m). Each focal sampling period was set as 10 min and the order of the focal individual was selected by random drawing . When anWe ranked the aggression-submission bouts of adult males to determine their rank order of dominance. The number of wins and losses in conflicts was put in a paired interaction matrix, and the rank order was determined by minimizing the number of wins below the diagonal. We converted these sequential levels into proportional levels, which ranged from 0 (lowest level) to 1 (highest level). Based on the index of aggression-submission behavior and the win-lose ratio, David’s score (DS) was calculated to determine the ranking among individuals . David’sHere, We recorded the ranks of adult male Tibetan macaques in the mating season, as shown in The strength of the social bond between males was measured by determining the individual intimacy index  to evaluate the social relations of males in a group . DSI indd represents the number of behaviors constituting the index , I of dyad x y, and I in all dyads social relationships.Here, We used agonistic support among males as a behavioral indicator to quantify coalition behavior. In social animals, agonistic support occurs when an individual participates in an ongoing conflict between two individuals and supports one of them while attacking the other . During We performed ad libitum sampling to record the data of agonistic support. The identities and behaviors of recruiters, targets, supporters, and bystanders in the attack support events were recorded .When an attack occurs, two attacking individuals are defined as the recruiter and the target. A recruiter was the one who actively sought agonistic support during a conflict . A target was the one who was attacked in a coalition attack. Supporters were individuals who joined one party to attack the other during a conflict. A bystander was an individual who was present within 10 m of the conflict but did not attack.Additionally, unsuccessful attempts to recruit supporters were also recorded. Recruitment failed when bystanders avoided  and ignored  attempts of males to recruit allies.We performed Spearman’s rank correlation analysis to determine the association between DSI and the formation of the male coalition. We used generalized linear mixed models (GLMMs) with the binomial connection function and the Poisson distribution function to study the factors influencing the coalition and the choice of allies of the supporters  with the link functions Logit and binomial as distribution functions to determine the factors that influence the selection of partners by supporters. We evaluated whether the ranks of attackers and the DSI scores of supporters and attackers affected the partners selected by the supporters (N = 100). We analyzed each coalition event twice, one of which was whether the supporter and the recruiter were in coalition, and the other was whether the supporter and the target were in coalition. The predictive variables were (1) the rank of the attacker  and (2) the DSI scores of the supporters and the others participating (the recruiter and target) in the conflict. The response variable was whether a male from individuals in conflict was selected as a coalition partner by the supporter (yes/no). To examine the influence of the competitive nature of coalitions we first ran the model including the rank difference between the recruiter and target as an interaction term with the two predictor variables above. If the interaction term was not significant, we then constructed a simplified model that only contained the main effects and analyzed the new model.Model 2: Was the participation of supporters related to DSI, residence time, or age?We constructed a generalized linear mixed model (GLMM) with link functions log and Poisson as distribution functions (N = 72). The number of agonistic support events from individual I to J was used as the response variable (“I” is the supporter and “J” is the recruiter). The predictive variables were the residence time/the age of individual I and the DSI scores of individuals I and J. We divided the age of adult males into young adults, middle adults, and old adults see .Model 3: Decision-making of partner choice in coalitions We used the generalized linear mixed model (GLMM) with the link function Logit and binomial as distribution functions to evaluate the specific influence of social bonds and social rank interaction terms on coalition formation. We used a dataset containing data on adult male coalition (N = 120). The predictive variable was the interaction of social bond and social rank, and the response variable was the success of the coalition (success: yes/no).The study was completely observational in nature and did not affect the welfare of the Tibetan macaques. This study was conducted with the Huangshan Monkey Management Center and the Huangshan Garden Forest Bureau. It complies with the regulations of the Chinese Wildlife Conservation Association regarding the ethical treatment of research subjects, and under the law of the People’s Republic of China on the protection of wildlife. We calculated 55 DSI scores and found that the social relationships among males were highly differentiated, as shown in p < 0.001). Additionally, bystanders ignored and avoided a male’s request for support 8 times.We recorded 93 incidents of male agonistic support. The DSI score was significantly correlated with the times of coalition , and potential supporters chose high-ranking individuals as allies during a conflict .Model 2: The joining of supporters and social bond, residence time, and ageWe recorded 65 events of coalition behaviors of adult males. According to the results of Model 2 , the malModel 3: Decision-making of partner choice in coalitions z = 1.650, p = 0.099). We used logistic regression to fit the interaction between social rank and social bond. The model predicted that supporters were more likely to support individuals with the highest social bond strength and the highest social rank and that the score was a linear combination of their rank, social bond strength, and mutual effect  , high levels of cooperation were maintained in an iterated Prisoner’s Dilemma game only when interacting with their social partner [Affiliative relationships of macaques were stable during the mating season in our study. Social bonds and coalition frequency are often correlated . Our res partner . Thus, sOur research showed that social rank had an influence on coalitions, and males were more likely to choose high-ranking allies. The tendency to form coalitions with high-ranking males has been demonstrated repeatedly . During Crocuta Crocuta), social rank and social bonds have been shown to be important in soliciting help or predicting competitor’s supporters [In Tibetan macaques, coalition formation seemed to be based on two criteria . We use multiple logistic regression to model decision rules in which individuals combine different types of social information. The results revealed that the partner choice in coalitions is related to social relationships and rank. Males choose the coalition that works best for them according to different situations ,37,38. Ipporters . These rIn conclusion, social bonds and social rank both influence the formation of coalitions in Tibetan macaques. Our results suggest that males are more likely to select allies who have a high rank and strong social bond to reduce the risk of ally defecting and increase the success rate of attack. Therefore, males choose different coalition strategies based on the competitive environment to increase coalition advantages. The study of male coalition behavior in multi-male and multi-female societies provides a new perspective and theoretical support for understanding the diversity of primate social behavior and the evolution of social structure. Further research on more social behaviors between males is needed to better understand the evolution of male coalition in primates.