Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value. The process of human cognition of the environment begins with the acquisition of multiple forms of redundant and complementary information by means of perception, such as sight, sound, touch and taste. This information is then processed by the brain to obtain human understanding and perception of things. This process of moving from perception to cognition is the process of fusing information from multi-sources . Since eData fusion research first originated in the military field, and foreign research on data fusion is more comprehensive and advanced. The first use of data fusion methods by Bar-Shalom in the 1970s led to a significant improvement in the performance of target detection and tracking . In the Since Germany put forward its Industry 4.0 strategy in 2014, countries such as the US, Japan and China have also launched their own national development implementation strategies for manufacturing. The core technology of the strategies of all countries can be summarized as the conversion of data into intelligence through data fusion technologies. Due to the rapid development of industrial internet technology, the traditional manufacturing model and industrial ecology has been greatly transformed, and digital transformation is urgently needed. Data are the core asset of an enterprise, and the flow of data is critical and decisive throughout all processes. Where digitalization solves the problem of data sources, networking solves the problem of data flow, and intelligence solves the problem of autonomous data flow. The series of processes that combine these aspects of the three rely on the extraction and fusion of the knowledge implicit in the multi-source data. This is not only a hot issue in computer science, but also a key to achieving the transformation of China’s manufacturing industry and the significance of this paper’s research .Data fusion is the utilization of the complementary information between different data sources to obtain a more complete and adequate representation and to enhance the performance and explainability of the fused model ,11. ExisData-level fusion is the lowest level of fusion that means the fusion of the collected raw data after simple pre-processing, including weighted averaging, wavelet transform , HIS traFeature-level fusion methods are the direct serial fusion of features from all data sources. Such methods include Kalman filtering, the entropy theory method , the artDecision-level fusion originated in the field of pattern recognition and is also known as semantic-level fusion . It usesFrom the above study, it is clear that compared to feature-level fusion and decision-level fusion, data-level fusion is less stable, and the fused data requires the same type of data and the algorithm is less robust. Current research on multi-source data fusion therefore focuses on feature-level fusion and decision-level fusion .Most traditional multi-source fusion methods use simple voting, averaging and ranking methods, which are simple and easy to implement for modelling, but the performance of the fused model is poor. Therefore, the current mainstream multi-source fusion method uses ensemble learning as the guiding idea for fusion. Xiao designed a combination strategy of ensemble learning, which combines a KNN (K-nearest Neighbor) with an SVM (Support Vector Machine) to obtain a better fusion output and improve the robustness of the model . Amin etThe existing algorithms do not take into account the characteristics of the data and the long-tail distribution of the data and the uneven difficulty of sample identification due to small sample data. The direct fusion of data from multi-sources leads to poor model performance.Inconsistent data scales between multi-source data make it difficult to fuse the models and the low accuracy of the fused model.To address these problems, Shang et al. proposed a multi-scale deep feature fusion method based on information entropy . MethodsThrough the study of these two aspects, this paper addresses the difficulties of multi-source data fusion in industrial production applications, realizes digital management and intelligent decision-making of multi-source data fusion, and supports the digital transformation of China’s manufacturing industry.For the characteristics of local correlation of image data and sensing data, this paper designs the maximum average pooling (Max-AVG pooling) feature extraction layer and residual dilated convolution feature extraction layer, respectively. This paper combines the proposed transfer learning VGG19 multilayer feature fusion network to solve the problem of small sample data, to achieve the multilayer feature fusion of data and to improve the robustness and accuracy of the model.In response to the problem of inconsistent data scales of multi-source data, which makes it difficult to fuse models and poor performance after fusion, research on decision-level fusion methods for multi-source data has been carried out. In this paper, an improved entropy-weighted topsis method fusion evaluation system and dynamic fusion strategy are proposed to eliminate the models with poor performance before fusion.Synthesizing the previous studies, this paper proposes an improved entropy-weighted topsis method of multi-source data fusion evaluation system, whose main contributions are summarized as follows.The remainder of this paper is organized as follows. In Data in intelligent production is an overall reference to the various types of multi-source data generated in industry around the full product lifecycle of intelligent manufacturing process. Multi-source data are data from different sources that are collected in one dataset . The objThe data sources and modal classification criteria for intelligent production show that there is a correlation between multi-source and multi-modal . Based oMulti-source multi-modal data are all types of production data collected by several different sensors of different types, and are also referred to as multi-source data. In practice, the data may be unavailable due to noise and large-scale missing issues that cannot be removed from different modalities and sources. As multi-source data can provide more information than single-source data, by complementing and correcting each other’s data, they can provide more accurate information to validate the above problems. Therefore, the research on the fusion of multi-source data is also a hot and difficult topic in the current academic research.Multi-source data fusion focuses on how to fuse multiple data with certain architectures and methods to achieve a unified representational framework that contributes jointly to the target task. In this paper, the fusion architectures for multiple sources are directly divided into three types: joint architecture, collaborative architecture, and encoder and decoder architecture .(1)Joint structure.The strategy of the joint architecture is to map the coded representations of information from each single data source into the same shared semantic subspace in order to fuse different types of features. The core idea is the association of features through feature weighting or certain rules to achieve the union of features. The joint architecture has the advantage over other architectures of having a simple integration method and a shared space with semantic invariance. However, this architecture requires a high level of semantic integrity of information across data sources, and problems of incomplete or inconsistent data can be magnified in the later stages of fusion, affecting the effectiveness of the fusion.(2)Collaborative architecture.The collaborative architecture is the synergy of information from a single data source with each other through the action of different constraints. The goal of the collaborative architecture is to transfer knowledge and coordinate associations across data source domains. Because of the differences between the information from the data sources, the collaborative architecture can maintain characteristics unique to each data. Collaborative architectures have the advantages of independent operation of models constructed on each data source and low quality requirements for data sources, and are widely used in cross-modal learning . However(3)Encoder and decoder architecture.The encoder and decoder architecture is used when mapping one data source modality to another target space modality transformation and consists of two parts, the encoder and the decoder. This architecture has the advantage of being able to generate new target space samples based on the source modalities, however each encoder and decoder can only encode one type of data. This architecture can be seen as a special collaborative architecture designed for specialized data, with a complex and poorly generalized module design.(1)Selection of fusion architecture.From the above study of the three fusion architectures, it is clear that each has its strengths and weaknesses and that there is no one architecture that is completely universal and performs well for all data. One of the problems facing multi-source data fusion is how to choose the appropriate architecture based on the characteristics of the multiple sources of data in production. A comparison of the advantages and disadvantages of different fusion architectures is shown in Due to the special character of industrial big data, which is highly susceptible to noise, missing data and data anomalies, it is not suitable for fusion using a joint architecture model built on a unified structure. There are primary and secondary relationships between multi-source data collected at different ports. Establishing models that run independently on each data source not only maintains the unique characteristics between the data, but also allows the selection of appropriate algorithms to model the data in accordance with the characteristics of that data source ,46. The (2)Selection of fusion method.Feature-level fusion methods take full account of feature interactions and correlations between data sources and have less information loss and model performance loss, but require longer data processing times and are more sensitive to data quality problems. Decision-level fusion methods are well adapted to the problems of low data quality and missing data in industrial data, although the loss of information and model performance is high. Therefore, the fusion method in this paper uses decision-level fusion methods.Deep learning has also played a powerful role in multi-source data fusion methods in recent years, and some of the literature also refers to deep learning multi-source data fusion models as model-based fusion methods . These fWith the rise of artificial intelligence, deep learning feature extraction algorithms are gradually replacing traditional feature extraction algorithms. Deep neural networks pair convolutional kernels of different sizes with activation functions in order and hierarchy to build different feature extraction and activation layers to complete automatic feature extraction. The feature extraction algorithms of deep learning are highly adaptable and accurate, and the learned features have certain semantic characteristics, which makes deep learning successful not only in the field of computer vision, but also in fault diagnosis , acousti(1)Multi-channel Max-AVG pooling feature extraction layer.Branch 1.The original features are passed sequentially through a convolutional layer of size Branch 2.The original features are passed through a convolutional layer of size Branch 3.The original features are sequentially passed through a convolutional layer of size By observing the image of the surface of the target to be inspected, it can be seen that the surface defect areas are brighter or darker than the defect-free areas. Based on the above problem, a maximum average feature extraction module is designed in this subsection. The module has three branches, as shown in The raw feature data are processed in 3 different branches and finally connected by the concate fusion layer by channel to provide the fused feature data for the later model.(2)Residual dilated convolution feature extraction layer.As data features such as image, audio and vibration mostly satisfy local correlations, the association between features is mainly local and extends from the local to the whole. This type of locally relevant data feature extraction is suitable for the use of RNN  models represented by LSTM (long short-term memory) and others. However, as RNN models cannot be parallelized, they are very time consuming for long sequence data. Therefore, this subsection proposes a residual dilated convolution feature extraction layer for long sequence data by using the feature that CNN models can be parallelized. It also combines different scales of the dilated convolution layer to increase the perceptual field of the feature extraction layer without increasing the network parameters, and extracts feature data at different scales. The module has 2 main branches, as shown in Branch 1.The original features are passed directly through the intermediate layer without processing and finally fused with the processed feature data by channel connection. This branch is mainly used to prevent overfitting of the model due to too many layers. This branch can also be used to enrich the original features by increasing the number of channels of the original input through a convolutional layer of size Branch 2.Module divides the same input into two The raw feature data are processed in two different branches and finally fused by the connecting fusion layer by channel to provide the fused feature data for the later model.(3)A feature adaptive mapping layer.After feature extraction is completed, the neural network uses essentially the exact same non-linear transformation to learn the same type of feature data that was extracted. However, due to the different operations, the differences between the feature data, even under the same operating conditions, can be large. This would make it difficult to classify these feature data into the same category. To address this problem, this subsection introduces a feature adaptive mapping layer that automatically learns to perform non-linear mapping based on the input data features . The modBranch 1.The original features are passed directly through the intermediate layers without processing and finally fused with the processed feature data by channel connection. This prevents the model from being overfitted due to the number of layers. Similarly, this branch can be enriched by increasing the number of channels of the original input through a convolutional layer of size Branch 2.The same input is divided into two channels for ReLU (Rectified Linear Unit) regularization and minimization feature extraction , where t(4)Time-series data image conversion processing based on the Gramian angular field.Deep learning techniques are developing rapidly at this stage, especially in the field of imaging, and have made great achievements. For digital signal processing problems, the use of deep neural networks is extremely limited. In computer vision terms, image data is not necessarily a photograph or video, but can be ’image’ data from infrared sensors, accelerometers or other sources. By transforming time series data into images, the advantages of deep learning techniques for image processing can be fully used. In this paper, the Gramian angular field (GAF) algorithm is introduced to convert digitized signals into image data for processing. The Gramian angular field algorithm is suitable for real-time high-frequency signal processing because it can keep the data features uncorrupted while preserving the original time-ordered information .Transfer learning is a method of learning from a previously similar task and applying it to a new one. The aim is to extract knowledge and experience from one or more source tasks and apply it to a new target domain to reduce the cost of model training . Thanks As shown in Model performance and error are closely related and quations  are usedFor the problems of long-tailed data distribution and uneven sample identification difficulty caused by small sample data, this section solves such problems well by introducing the focal loss technique , as shown models to be evaluated and m evaluation metrics in the topsis evaluation system, where m evaluation metric of the n model to be evaluated. The algorithm flow is as follows.For the construction of the evaluation system, this paper uses the topsis  method. The topsis method is a commonly used comprehensive evaluation method that makes full use of the information in the raw data and accurately reflects the gaps between the evaluation options.Assuming that there are Indicator attributes are positively processed, converting all low and medium performance indicators into high performance metrics.Z.Normalization of the data to obtain the normalized matrix Z and the inferior solution Z.Determine the optimal and inferior solutions. The optimal solution Calculate the distances Calculate the proximity of each evaluation object to the optimal solution However, the topsis method is established on the basis that the individual evaluation indicators are all equivalent, which is not objective in use. Weights need to be added between the evaluation indicators to reflect the differences between them. There are currently two main methods for increasing the weight between indicators, the analytical hierarchy process (AHP)  and the nth model to be evaluated under the m evaluation indicator for that indicator.Calculate the weight of the j.Calculate the entropy value of indicator Calculation of information entropy redundancy (degree of dispersion).The final entropy weight values for each indicator are obtained as follows.In order to measure the contribution of each model before fusion, this paper specifically establishes the contribution percentage Equation  as folloThe improved fusion evaluation system screens for differences in the performance of models on different data sources and removes poor performing models from the fusion system when they appear. Only the models with better performance are retained as the final fusion models. This not only reduces the number of models to be fused and reduces computational effort, but also improves generalization accuracy and model comprehensibility, ultimately improving fusion performance.For the model fusion of multi-source data, the augmentation method strategy of integrated learning assigning different weights to different sub-models has some reference value. The multi-source data fusion in this section is based on the AdaBoost algorithm and combines its findings with multi-source data fusion. The main idea of the AdaBoost algorithm is to assign higher weights to classifiers with high accuracy, and eventually multiple classifiers are weighted by a weighted set to become a strong classifier with high accuracy . The algL trains a total of R iterations, where the training samples are all given the same weight For the training dataset Rth iteration.Several iterations were performed to learn using a training dataset with a weight distribution i sample.Calculate the classification error rate of Calculate the coefficients of the weak classifier Updating the weight distribution of the training dataset for the next iteration.Combination of individual weak classifiers.To obtain the final strong classifier.However, the AdaBoost algorithm is sensitive to noisy data, which can lead to disadvantages such as higher weights in iterations and more time-consuming fusion training, affecting its final performance. These problems also create challenges for the fusion of multi-source data and require improvements to their fusion strategies.(1)Improved evaluation indicators.i is the predicted object, j is the jth element of the predicted object, and K is the number of categories of the multi-classification problem.In general, Equation .(25)PcPcDynamic fusion strategies.For the fusion integration of several data sources and models, the inference time increases exponentially with the number of fused models. Selecting after each iteration use the fusion evaluation system would be extremely costly in terms of computing power and time, and would result in incorrect selection due to some anomalous data. To address this problem, the dynamic fusion strategy in this subsection calculates the accuracy, the mean confidence As shown in The algorithm in this paper runs on a Windows 7 PC with an I5-8500 processor, NVIDIA GTX1050 graphics card with 4G capacity, 32G of RAM and 1T of hard disk. The algorithm programming is implemented using the Python 3.6 programming language, Keras and the TensorFlow 1.15 framework. End mills dry cut 45 steel workpieces with dimensions of 100 mm × 100 mm × 20 mm under the condition of no coolant. Four-flute carbide milling cutters with an 8 mm cutting edge, 24 mm cutting edge length and 60 mm total length are used. The vibration signal is acquired by a WB1550T sensor and the milling force signal is acquired by a Kistler 9257b sensor, with a signal acquisition frequency of 20 KHz for each channel. The spindle speed of the machine is 2000 rpm, the feed rate in X-axis direction is 800 mm/min, the cutting width is 8 mm, and the cutting depth is 0.2 mm. Each milling 80 mm is recorded as one tool walk and the In this paper, the mean value of As shown in The experiments in this chapter are divided into experimental validation of multilayer feature fusion on a single data source and decision-level fusion on multi-source data. Experiments on a single data source are subdivided into two parts. Part one, Max-AVG pooling multilayer feature fusion network in (1)Transfer learning VGG19 multilayer feature fusion network.This subsection uses the transfer learning VGG19 multilayer feature fusion network structure in (2)Max-AVG pooling multilayer feature fusion network for tool wear diagnosis.Considering the characteristics of the target surface area to be detected, the Max-AVG pooling feature extraction layer and the feature adaptive mapping layer from As shown in (3)Performance comparison of two improved network models.As it can be seen from The Max-AVG pooling multilayer feature fusion network outperformed the pre-modified network in all metrics, and effectively suppressed the overfitting problem of small samples and long-tailed distribution data sets.(1)Improved transfer learning VGG19 multilayer feature fusion network.In this section, we use the Gramian angular field algorithm to convert the milling force signals collected in four tool wear status into images, and continue to diagnose the tool wear status using deep learning techniques. As the milling force signal is collected at a frequency of 20 KHz, the spindle speed is 2000 rpm which is 0.03 sec/rev. The sensor is capable of collecting approximately 600 force signal points during one cycle of spindle rotation. Taking into account various noise interference factors, the sampling points of the data in this subsection are taken as 600, 900, 1200 and 1800, respectively, to alleviate the model overfitting problem through multi-scale data generation. In The learning rate of the network is set to 0.01, the decay momentum is set to 0.9 and the number of training iterations is set to 750 epochs. A total of 36,000 sample images are generated for each of the four tool states, of which 80% are used as training data and 20% as test data.(2)Improved residual dilated convolution multilayer feature fusion network for tool wear diagnosis.For the above problem, the residually connected dilated convolutional feature extraction layer in (3)Performance comparison of two improved network models.As it can be seen in The improved transfer learning VGG19 multilayer feature fusion network suffered from overfitting problems during the training process. Although the improved residual dilated convolution multilayer feature fusion network is slightly lower in performance than the pre-improvement network, the stability and convergence of the model are better. This is even more practical for raw signal data containing complex noise.(1)Simplified VGG19 multilayer feature fusion network.This subsection of the experiment introduces seven data sources, such as milling forces in the X/Y/Z axes, vibrations in the X/Y/Z axes and images of surface knife mark. To facilitate feature extraction and the fusion of sensor data by deep neural networks, the Gramian angular field algorithm is used to transform the sensor signal data. A total of four tool wear statuses for each of the six sensor sources generate 9000 sample images, for a total of (2)Comprehensive evaluation of the performance of the dynamic fusion process of multi-source data.The multi-source data fusion is mainly designed to solve the four tool state diagnostics with five metrics: the accuracy of the test data and the average of the four confidence levels As can be seen from the table above, the percentage contribution In As it can be seen in To further validate the effectiveness of the improved fusion evaluation system and fusion strategies, this paper tests them compared with the current mainstream multi-source data fusion algorithms ,34,35. TIn summary, the following can be concluded.Simplification of each independent VGG19 multilayer feature network without the use of specially designed extraction layers can reduce the difficulty of model structure design and training deployment.The collaborative architecture fusion method has significantly higher accuracy than the joint architecture fusion method and is more suitable for constructing fusion models for multi-source data, but its average single inference time is longer.Screening after each iteration using the fusion evaluation system not only takes too long to train the network, but also causes screening errors due to anomalous data. A dynamic fusion strategy is added to the fusion evaluation system to remove data sources that impair the performance of the fusion model. This effectively alleviates the shortcomings of the fusion model in terms of data source quality, model quality, and training inference time.To address the problem of inconsistent data scales among multi-source data, which makes the established models difficult to fuse and low fusion accuracy, an improved fusion evaluation system is added to the decision-level fusion algorithm, and a dynamic fusion strategy is proposed. The improved fusion evaluation system integrates the dynamic fusion of model performance on different data sources, making the fusion model insensitive to hyper parameter selection and improving the accuracy and robustness of the fusion model, and obtaining optimal fusion results. The effectiveness of this paper’s improved entropy-weighted topsis method of comprehensive evaluation system is subsequently verified experimentally on single-source data and multi-source data respectively. Compared with the current mainstream multi-source data fusion methods, the multi-source data fusion system and fusion strategy proposed in this paper do not use specially designed feature extraction networks, the model training deployment is less difficult, and the training inference time is shorter. Our fusion method has better performance and can be well adapted to the characteristics of industrial data.However, there are still some limitations in this paper. Although some results are achieved in this paper, the paper focuses on validating the application of multilayer feature fusion in production through a typical VGG19 network and its modifications. In the study of decision-level fusion, this paper mainly considers the performance of fusion from an algorithmic point of view. Further research is needed on the distribution of raw data and quality improvement issues on data collection, which are also the direction of subsequent research in this paper.


Current research has confirmed that the quality of the caregiver-child relationship influences the child’s emotional and behavioral development. Play and feeding contexts, for example, are the best contexts to observe mother-child or father-child interaction. The observation of feeding interaction establishes involvement on the part of both parties and identifies relationship characteristics. The purpose of this study is to select and describe the most frequently used observational methods during feeding interactions in the first three years of a child’s life. Instruments that employ video recordings of mealtimes will be detailed to highlight the relevance that specific tools have nowadays. Finally, the SVIA , a technique for analyzing food interactions by observation that has also been utilized remotely, will be offered. This is intended to provide practitioners and researchers with an overview of tools while also taking into consideration the present scenario in which digital tools are increasingly being employed in health and clinical settings. Furthermore, the purpose of this paper is to review the various observational methods of the parent-child relationship to assist future practitioners and researchers in the field in making an accurate assessment of caregiver-child interaction and selecting a valid tool for the early recognition of problematic relationships and identifying the most appropriate treatment modalities. Parent-child interactions are essential as they form the basis for the child’s later emotional and cognitive development and serve as a predictor for children’s experiences with the outside world . ParentaIn recent years, research has focused on the role of mothers and fathers in the adaptive emotional and cognitive development of the child, particularly the role of fathers as protectors and/or risk factors for the development of difficulties in children. In the context of feeding, researchers have mainly focused on mother-child interactions, as mothers are believed to be primarily responsible for feeding children . AccordiObservation of the interaction between parents and children is essential to identify difficulties and strengths in the relationship, as these can have an impact on the child . Many auOne of the key features of parent-child interaction observation tools is that they allow the measurement of dyad-level constructs, such as synchrony or reciprocity because they focus on the coordination of behaviors between the child and the parent. This is critical because parent-child synchrony has been shown to be related to children’s later cognitive development ,11.The literature shows how important it is to observe relationships, as humans are born with an innate sensitivity to connect with others and share their experiences. According to Trevarthen , throughAccording to Corcoran & Fischer , observaThe use of reliable and valid measures to assess the quality of parent-child interaction is essential because only in this way can strategies be provided that can identify non-functional care practices and, therefore, make possible interventions that can improve relationships. Indeed, it has been observed that compromised relationships can lead to the development of psychopathological symptoms as children mature . To idenIn this paper, we chose to focus on the qualities of interaction during feeding since in early childhood this activity contributes to the construction of reciprocity in exchanges between parents and children . During If the caregiver-child dyad is not attuned and does not communicate effectively, it will be difficult for the child to use self-regulatory mechanisms, and this may negatively affect the child’s behavior and emotional state in the future ,22,23. SObservational-type tools have been in use for some time, but the impact of the COVID-19 pandemic on research has brought new opportunities, especially regarding the use of online tools. In fact, while the possibility of using psychoeducational tools and online clinical sessions for parents and families existed before the spread of the pandemic, the advent of COVID-19 introduced greater use of digital tools and allowed for a different management of time and costs due to travel for health care. For example, some research groups, mentioned in this review, had already experimented with the online use of an observation tool in the context of food interaction. The SVIA scale, during the COVID-19 pandemic, began to be administered remotely with satisfactory results.Considering this background, this review aims to select and compare strategies for assessing caregiver-child feeding interactions. In particular, this review aims to describe tools that employ video recordings of meals to highlight the relevance of this type of specific tool.The objective of this paper was to learn about observational tools currently used via videotaping to assess eating disorders during childhood. The methodological approach used consists of a narrative review ,26, a foInstruments were included that (1) were quantitative measures, (2) could be used for infants (age 0–4 weeks) and/or toddlers (age 1 month–1 year) and/or children (age 1–2 years) and/or preschoolers (age 2–6 years), and (3) were used to observe parent-child interaction during mealtime with videotaping. Non-observational instruments (1), which did not investigate parent-child interaction (2), were excluded; in addition, instruments for which a reference describing their psychometric validity could not be found (3) or which had not been evaluated by an experienced external observer (4) were excluded. Articles were selected based on their impact on the scientific community, trying to select them according to the impact factor of the journal in which they were published and according to the number of citations to have high-quality articles. However, because this topic is specific, it was necessary to expand the literature to be considered and it was not always possible to meet the criteria for the number of citations. The decision to include in at page 8, other studies (selected according to the above criteria) that have used the tools in the last 3 years, is because we tried to gather information on the current situation through the most recent studies that had a focus as consistent as possible with the topic of this review, also concerning the use of these tools during the particular period of the COVID-19 pandemic.The research conducted identified several studies using mealtime observation tools with the use of video recording. The details are shown in Below is a brief description of the tools included, grouped according to the following age groups of the child: newborn (ages 0–4 weeks); infant (ages 1 month–1 year) toddler (ages 1–2 years); preschooler (ages 2–6 years).The Assessment of Mother-Infant Sensitivity (AMIS)  scale meThe BATMAN (Bob and Tom’s method for assessing nutrition)  is used The Dyadic Interaction Nomenclature for Eating (DINE)  targets The Mealtime Observation Schedule (MOS)  has beenMother-Infant Feeding Tool (MIFT)  is for nThe NCAST developed by Barnard , is distThe Parent Child Early Relational Assessment (PCERA)  is an obResponsiveness to Child Feeding Cues Scale (RCFCS) , is usedThe Mealtime Interaction Coding System (MICS)  is used The Lausanne Trilogue Play (LTP)  is used The Feeding Scale  and its The role of the caregiver is crucial. According to Bowlby , a succeThe purpose of this review is to identify observational tools for assessing parent-child feeding interactions, to present information to clinicians and researchers in the field, and to find strategies for assessing and observing the dyadic (parent-child) relationship. Because caregiver-child interactions are essential to the child’s later development, assessing the quality of these interactions could be useful in preventing childhood eating disorders and future psychological problems in the child. By observing these moments, both strengths and weaknesses can be identified. In fact, the use of observational tools can be useful to researchers and clinicians who wish to understand both mealtime and relationship quality and how parents and children mutually regulate eating behavior. This review will help researchers more quickly identify which tool to use in cases of early feeding problems in early childhood and analyze the feeding behaviors that can make a difference in the adaptability of the feeding process and feeding outcomes. Indeed, through early assessment of parent and child adaptive processes that occur in real-time during feeding interaction, it will be possible to address critical areas for caregiver-child dyads.One aspect to consider is the need for specific training to use the instruments identified in this review. Indeed, to obtain a valid observation, and thus to score it in a way that reflects what was observed during the recording, it is important to have appropriate training and to pass a reliability test. Because caregiver-child relationships and interactions during feeding are complex to analyze and interpret, it is necessary for specialists to be knowledgeable about them and to conduct reliable assessment procedures. All scales examined in this review  require specific and/or certified training by the coder. This reinforces the veracity and reliability of the results, although training procedures can sometimes be expensive and time-consuming.Comparing the observational power tools on training shows the following: (1) The AMIS tool needs training and evaluation criteria to provide a basis for training potential users. (2) In the BATMAN instrument, observers are taught the operational definitions of behavioral categories and general procedures for recording data; coders practice coding recorded videotapes until they reach an agreement of at least 0.90. (3) DINE coders were trained on the DINE using the procedures established by the authors themselves until they independently achieved 70% reliability on the videotape. (4) Coders on the power scale and SVIA periodically conduct a training course on the application of the instrument in normal and clinical populations. The course includes an evaluation of observer reliability against the Scale coding system. (5) Coders have been trained in the MOS over the course of several sessions and meet periodically during coding to minimize errors; two experienced observers trained in the use of the instrument code video recordings of parent-child interaction, and a third observer verifies the reliability. (6) All videotapes were coded by one MIFT-trained coder; a second coder trained to 80% reliability coded a random 20% of the videotapes. (7) To be qualified to perform NCAST scoring, attendance at a workshop conducted by an NCAST-certified instructor is mandatory; inter-observer reliability of 85% must be achieved to use the scales in clinical work and 90% for use in research. (8) An observer trained on the PCERA explains to others how to score according to the PCERA manual. (9) RCFCS coders receive training for a period of 2 weeks before coding begins and are required to go through training to code 85% of gold standard videos; usually the training lasts 3 months, and coders meet during the week to address and solve problems that arise during the coding process. (10) Coding for MICS is performed by trained independent coders who are blind to the study hypotheses. (11) Finally, coders are also required to perform training when evaluating videos according to the LTP scale.The SVIA , an ItalThe SVIA has been used for more than 20 years to assess the quality of dyadic exchanges between parents and children during feeding. Recently, during the COVID-19 pandemic, it began to be administered online, through the use of digital tools, and the results were positive overall. In the study conducted during the COVID-19 pandemic , the autIt would be useful in the future to experiment with using the other tools described above remotely, to have greater flexibility in their use and to extend the possibility of participation to families unable to move.This review has some limitations. First, it is not a systematic review, so it does not follow a systematic approach. However, we believe it can be useful as it describes observational tools used through videotaping and thus potentially usable at a distance. Second, our focus has been only on child-parent(s) interactions, but it is worth noting that there are several reference figures such as parties, relatives, and siblings. Moreover, another limitation is related to the age group: it would be interesting to investigate observational-type instruments for school-age children but also adolescents.Future studies could investigate these limitations and also evaluate the differences between online versus live observation on the assessment situation, in fact, it is possible that observation of parents’ interaction with their children would yield different results in an online context than in a live observation situation because the people being observed might be influenced by the camera differently than a live observer .Studies conducted by Infant Research have explored the characteristics of the caregiver-child relationship, highlighting the skills the child needs to create and maintain relationships and the importance of parenting skills in caregiving. The observational coding system makes it possible to describe the process involved in parent-child feeding interactions and to describe the sequence of parent-child interaction behaviors. Observation of early parent-child interactions, which in the first years of life occur during play or feeding, can help identify and prevent early problems in the dyadic relationship, helping families who may benefit from interventions such as parenting support, before the child’s problem behaviors become established ,90. ObseIn the future, it would be useful to make a comparison between these two methods, (in-person vs. online) so that practitioners and researchers can avoid being influenced by cost or time and have more options according to the needs of families, hospitals, and specific economic and social situations, choosing the most appropriate observational method to obtain concrete and satisfactory results.


Plant point cloud segmentation is an important step in 3D plant phenotype research. Because the stems, leaves, flowers, and other organs of plants are often intertwined and small in size, this makes plant point cloud segmentation more challenging than other segmentation tasks. In this paper, we propose MASPC_Transform, a novel plant point cloud segmentation network base on multi-head attention separation and position code. The proposed MASPC_Transform establishes connections for similar point clouds scattered in different areas of the point cloud space through multiple attention heads. In order to avoid the aggregation of multiple attention heads, we propose a multi-head attention separation loss based on spatial similarity, so that the attention positions of different attention heads can be dispersed as much as possible. In order to reduce the impact of point cloud disorder and irregularity on feature extraction, we propose a new point cloud position coding method, and use the position coding network based on this method in the local and global feature extraction modules of MASPC_Transform. We evaluate our MASPC_Transform on the ROSE_X dataset. Compared with the state-of-the-art approaches, the proposed MASPC_Transform achieved better segmentation results. Plant phenotype is to study how to measure the shape characteristics of plants, such as plant height, leaf organ size, root distribution, fruit weight, etc. These traits are closely related to the yield, quality, and stress resistance of plants. The study of plant phenotype has important value for agricultural modernization breeding , water aIn the process of plant phenotypic feature extraction, accurate segmentation of plant data according to different organs  is the premise of high-precision plant phenotype . Plant oBy summarizing the existing segmentation methods of plant point clouds, we find that the existing methods have poor segmentation effect at the junction of different plant organs. For example, in the segmentation result in the fifth row and the first column in Figure 7, some stems are erroneously recognized as leaves. And this phenomenon is more obvious when the stem contacts the leaves. In the segmentation result in row 6 and column 2 of Figure 7, the part of the small calyx in the point cloud is erroneously divided into leaves. The reasons for the above segmentation errors are as follows: (1) for the plant segmentation task, the points belonging to the same plant organ are far from each other and are interwoven with the point clouds of other organs. For example, in Figures 5 and 6 the stems of the plants are almost distributed in the whole point cloud space and interweaved with other organs. The segmentation network often extracts the point cloud features of the whole plant without distinction, and does not mine the relationship of point clouds belonging to the same organ in the point cloud space. (2) Plant point clouds have the characteristics of disorder and irregularity, which will affect feature extraction.To further improve the segmentation accuracy of plant point clouds, we use the Point Transformer  as the bWe propose a plant point cloud segmentation network named MASPC_Transform, and evaluate its segmentation performance on the ROSE_X dataset.We propose a loss function of multi-head attention separation based on spatial similarity. This loss can make the attention positions of different attention heads as dispersed as possible, and establish a connection for the point clouds that are far away but belong to the same organ, thus providing more semantic information for accurate segmentation.In order to reduce the impact of point cloud disorder and irregularity on feature extraction, we propose a position coding method that can reflect the relative position of points, and use the position coding network in the local and global feature extraction modules of MASPC_Transform.The main contributions of this paper are summarized as follows:The rest of this paper is organized as follows. Traditional methods achieve the segmentation of plant point clouds through geometric features . These mCompared with traditional algorithms, deep learning methods are data-driven, do not need too many artificial design features, and have better performance. Currently, the deep learning methods that have been applied to point cloud segmentation include methods based on multi-view , voxel , and poiAlthough great progress has been made in the research of deep learning segmentation algorithms for point cloud data, there are still few research on the segmentation of plant point cloud using deep learning methods. Wu et al.  adjustedGlobel) and local features (FLocation) are aggregated in the detection head and the segmentation results are obtained.The architecture of MASPC_Transform is shown in First, the plant point cloud is input to the location feature generation and global feature generation branches for processing. Both branches first extract the feature of point cloud. In the location feature generation, the PC-SortNet module can evaluate the importance of the features of different areas of the point cloud, and select the important features as the local features of the plant point cloud. In the global feature generation branch, the multi-scale grouped (MSG) feature extraction network can obtain the point cloud features of three scales to adapt to different sizes of plant organs. The features of the three scales are fused as the global features of the whole plant point cloud. We use the position coding proposed in this paper in PC_MSG and PC_SortNet modules. Position coding is discussed in detail in Multi-head attention  in MASPCQ, K, and V respectively represent the query matrix, key matrix, and value matrix of attention, and their matrix dimensions are ith attention head, In Equation (1), lization . S is deselfA, LGA, and crossA are derived from MHA. selfA can perform the calculation of multi-head attention among all elements of P, while LGA and crossA can handle different sets P and Q, and perform the calculation of multi-head attention between the two sets.In Equations (3) and (4), We proposed a multi-head attention separation loss based on spatial similarity loss in . This loδ is defined as follows:Plant point cloud data are a collection of a series of points in space. Point sets have the characteristics of disordered and irregular distribution, so we propose a unique point cloud position coding method. The position code contains the relative position information of each point and its adjacent points, so as to avoid the interference of the disorder of the point cloud on the feature extraction. Position code function δ of the point cloud space is composed of the relative position code (RPC) and the absolute position code (APC) of each point in the space. The absolute position code of a point is the coordinates of the point cloud. The relative position code of a point is the difference between the coordinates of the point and all points in its subspace. The relative position code keeps a certain invariance to the disorder of the point cloud, and it reflects the relationship between a point and its adjacent points, which can make the feature contain more local information. The position code network is shown in Suppose there are n points in the whole point cloud space. In Equation (5), In MASPC_Transform, we improved the MSG  in PointWe also improved SortNet in Point Transformer network , replaciWhen multi-head attention is used for feature extraction, there is a possibility that the generated multiple attention spaces are similar , which wIn Equation (6), Loss_CrossEntropy, the more realistic the prediction given by the network. As shown in Equation (8), we used the Loss_CrossEntropy and Separation_Loss as MASPC_Transform’s loss function. Where Loss_scal is the weight of Separation_Loss in the loss function. Using the new loss function to train MASPC_Transform can make multiple attention feature spaces specific.In Equation (7), We evaluated the performance of MASPC_Transform on the ROSE_X dataset . The ROSFor the model training, the Adam optimizer is used to update and optimize the network parameters. The initial learning rate is set to 0.001 and the batch size is 16. The GPU model is NVIDIA GeForce RTX 2080Ti, operating system is Ubuntu 18.04 LTS, CUDA version is 11.0. The proposed model is implemented in PyTorch with Python version 3.6. When training MASPC_Transform network, the input point cloud only contains three-dimensional X-Y-Z coordinates, and the number of input points is 2048.We use the Intersection over Union (IoU) and Mean Intersection over Union (MIoU) to evaluate the performance of all networks. Where IoU is equal to the ratio of intersection and union between the predicted point set and the real point set, and MIoU represents the average value of IOU of all categories. The higher the values of these two indicators, the better the segmentation effect of the point cloud. The mathematical definition is as follows:In In In order to show the segmentation effect of each method more clearly, we extracted some regions from the segmented plant point cloud and showed them more clearly in It can be seen from the segmentation effect shown in According to the experimental results in We propose a plant point cloud segmentation network named MASPC_Transform. In order to make the attention positions of different attention heads of MASPC_Transform as dispersed as possible, we propose a multi-head attention separation loss based on spatial similarity. In order to reduce the impact of point cloud disorder and irregularity on feature extraction, we use position coding in the local and global feature extraction modules of MARP_Transform. We evaluated the proposed MASPC_Transform on the ROSE_X dataset. The results of segmentation experiments show that MASPC_Transform network performs better than the state-of-the-art approaches. The results of ablation experiments demonstrate the effectiveness of the proposed position code and attention separation loss. Due to ROSE_X dataset is the only open source benchmark plant point cloud segmentation dataset, so the MASPC_Transform has only been tested on this dataset. If a new open source plant point cloud segmentation dataset appears, the MASPC_Transform should accept more tests.


There are several approaches for the calculation of capillary pressure curves in porous media including the centrifuge method. In this work, a new installation of centrifuge test is introduced and compared with the traditional setup. In the first setup, which is a standard approach in labs, the core face closest to the rotational axis is open to the non-wetting phase, while the farthest face is open to the wetting phase where strictly co-current flow is generated in rotations; labeled Two-Ends-Open (TEO). In the second setup, which is proposed as a new approach, only the outer radius surface is open and is exposed to the light non-wetting phase; labeled One-End-Open (OEO). This setup strictly induces counter-current flow. The two systems and their corresponding boundary conditions are formulated mathematically and solved by a fully implicit numerical solver. The TEO setup is validated by comparison with commercial software. Experimental data from the literature are used to parameterize the models. It is mathematically, and with examples, demonstrated that the same equilibrium is obtained in both systems with the same rotational speed, and changing the installation does not influence the measured capillary pressure. This equilibrium state is only dependent on the rotational speed, rock capillary pressure properties, and fluid densities, not the installation geometry, relative permeabilities, or fluid viscosities. However, the dynamic transition trend and saturation profiles were found to be dependent on the applied installation. It was observed that the OEO setup takes almost identical equilibration time as the TEO setup for mixed-wet states, although it needed much longer time in water-wet states. The presence of threshold capillary pressure significantly increased the time scale of the OEO setup. Also, it was found that in contradiction to the TEO setup, the dynamic saturation profile in OEO was rarely influenced by viscosity ratio. To conclude, the performed history matching analysis demonstrated that the OEO setup can be applied for the calculation of counter-current relative permeability from the production data with reasonable accuracy. •It is demonstrated that the same equilibrium was obtained in both OEO and TEO systems.•Only for the TEO case, the dynamic saturation profile was influenced by the viscosity ratio of fluids.•This OEO setup should help to measure counter-current relative permeability by an inverse calculation method.•Our analysis of the OEO setup showed that obtaining a unique relative permeability is guaranteed under different conditions. Special core analysis (SCAL); Centrifuge capillary pressure measurement; One-End-Open (OEO); Counter-current flow The capThere are several experimental methods for the calculation of capillary pressure curves in the laboratory including the centrifuge method, porous plate, and mercury injection or their combination with technologies like nuclear magnetic resonance techniques . This woCentrifuge tests also can be used as a wettability determination method as explained by This work will present a new design for centrifuge capillary pressure tests that helps in the measurement of capillary pressures in a different way and also the calculation of the counter-current relative permeabilities in imbibition or drainage processes. We will compare the traditional TEO setup against the proposed one-end-open (OEO) setup in the equilibrium and dynamic states. The sensitivity analysis on the impact of different parameters such as capillary pressure threshold, viscosity ratio, and wettability will also be carried out. The practical approach for the calculation of relative permeability will be investigated and the associated uncertainties will be discussed. The paper is structured as follows: The geometry descriptions, differential equations, and different boundary conditions are presented in the theory section Section . After t2w) phase saturation corresponding to zero capillary pressure. This situation corresponds to the state at primary or secondary drainage before any force is applied to displace the wetting fluid but after spontaneous drainage has allowed the sample to take up oil spontaneously. The core is located in a rotating system, on an axis termed x that is aligned outwards from the center of rotation, where x is positive outwards from the center and the core is mounted at x-axis to treat the system in one dimension. The centrifugal force enforces the non-wetting phase to enter the core and displace the wetting phase. In the drainage process, the capillary pressure resists this displacement.We consider two installations of centrifuge tests with different boundary conditions, applied to calculate drainage capillary pressure curves . The following description is illustrated in 2.1nw) low-density phase which extends outside the core to a free nw phase has pressure continuity in the space outside the core from nw phase has pressure continuity into the core only at ω, wetting phase is pushed out of the core at nw phase. By keeping the w fluid must leave through the same surface as the light nw fluid enters, namely at In the first system, termed Two-Ends-Open or TEO see a, the inere Pc=0 . The cor2.2ω angular speed (rad/s), K absolute permeability, i refers to phase-specific properties for the wetting We consider a system with immiscible and incompressible fluids, where the lighter fluid is non-wetting  while the dense fluid is wetting . The porous medium is assumed homogeneous and incompressible. Darcy's law in a rotating system is given by :(1)ui=−λtion law :(2)ϕ∂tpnw counter-current spontaneous imbibition system where a high saturation nonwetting phase is present in the core initially and a maximum wetting phase resides outside the core. It is a standard modeling assumption to set a zero boundary capillary pressure and let the mobilities be evaluated according to where the phases travel from, especially at the boundary, to allow flowing conditions . A numer2.4At hydrostatic equilibrium state of fluids , we have C is a constant of the second-order integration. For simplicity, it is assumed that the capillary pressure function C under this condition we obtain:nd order polynomial with distance, decreasing outwards. Hence, the two systems have the same distribution of capillary pressure and saturations at the same rotational speeds, regardless of the applied boundary conditions. At Since the wetting phase flux is 0 at equilibrium, Eq. This is the well-known Hassler-Brunner relationship .2.5During the forward simulation of centrifuge tests, the system of equations is solved numerically with a fully implicit (implicit pressure and implicit saturation) scheme which is outlined in detail in Appendix The fluid withdrawal rate, i.e., average saturation change rate  and strongly water-wet (sww) capillary pressure curves are spined off from the original curve by changing the model parameters. The capillary pressure curves are shown in The applied relative permeabilities a are ass3.2Pc_ww, and mixed-wet, Pc_mw). The rotational speed was selected similarly for all cases . The saturation distribution of phases after reaching the equilibrium is shown in In this part, the results of numerical simulation of centrifuge capillary pressure tests in both OEO and TEO boundary conditions, in their equilibrium state, are provided and compared. The capillary pressure curves are varied to obtain saturation distributions for different rock wettability conditions. Since both setups were acting similarly in their equilibrium state, it is expected that the Forbes methodology  for the Pc_MW) system and 1.4 days in the water-wet (Pc_ww) states. In the OEO system, although the equilibration process lasts 0.6 days for the mw case, it took around 360 days for the water-wet condition to meet the equilibrium conditions. It is clear that the time scale increases with water-wetness for both systems, however, it is more phenomenal in the OEO system. This time is especially significant at low rotational speeds where the mobility of a phase (here nw phase) is close to the endpoint  and the lowest ww condition is significantly influenced by the boundary conditions, while in the mw condition the difference is lower. The recovery rate  assumed constant , where the value of This noticeable rise in time is interpreted by the low  xth Eq.  is corre3.5Pc_ww) are used separately for each case.Flow in porous media is highly under influence of the viscosity ratio  data for the history matching task. In history matching, it was assumed that all parameters of flow are known, and only relative permeability variables are needed to be determined. Since finding the relative permeability parameters needs an iterative trial of simulations with different relative permeability realizations, the look-up process for the best values was managed by a PSO algorithm and a wide range of parameters . Full denw phase parameters  which can be an indication of the lower probability of calculation of relative permeability with the OEO setup.Before investigating the results of history matching, a sensitivity analysis on the impact of different relative permeability parameters  on the recovery curve of TEO and OEO setups was performed. 1.TEO: The centrifuge was spun only with a TEO setup.2.OEO: The centrifuge was spun only with an OEO setup.3.TEO & OEO: The centrifuge is spun for both TEO and OEO setups. So, the history matching is carried out for both setups at the same time. The average error for each setup is considered as the loss functionIn the following, the results of history matching are discussed, while the full details including figures are provided in Appendix These results were obtained after 800 unique simulation runs (for each setup) with different relative permeability curves, guided by the Particle Swarms Optimization (PSO) algorithm. As it is clear from the obtained results, in total, the accuracy of the predictions for all 3 cases is reasonable. A graphical presentation of the calculated curves and their Mean Square Error (MSE), Eq. To look deeper into the sensitivity of the obtained results to different parameters, the whole optimization task was repeated for cases with different viscosity ratios. The viscosity values, as well as the results, are summarized in 4In this work, a new installation for the centrifuge capillary pressure test labeled OEO was introduced and compared to the classic installation (TEO). Afterward, the behavior of these setups under different conditions was investigated and compared together. We showed that both systems provide similar results in the calculation of capillary pressure curves.2 in saline aquifers . The main difference between the two systems is the flow regime of the phases, where in the TEO system the fluids flow co-currently, while in the OEO system, the dominant flow regime is counter current. Considering these differences, the OEO installation is proposed for the calculation of counter-current relative permeabilities, which is essential in many disciplines such as migration of COaquifers , and hydaquifers .Here, the recommended technique for calculation of the relative permeability was trial-based history matching of the production profile. This technique could give a reasonable prediction of the relative permeability curve in hypothetical scenarios discussed in previous sections. The method was also recommended in the work of mw state, the time for both TEO and OEO setups is acceptable for both calculations of capillary pressure and relative permeability. In the ww state, the timescale of the OEO test is significant. So, in this wetting state running this test may be unfeasible. To save the testing time, the TEO setup can previously be launched to obtain the capillary pressure curve. It is important to note that if the capillary pressure curve is calculated in the TEO system, the OEO test can be launched with fewer numbers of rotational steps, so the test time/cost would be significantly reduced. Furthermore, since we showed that OEO setups are not sensitive to the viscosity values , the OEO setup may also be run with fluids with lower viscosities. We should also bear in mind that always there are methods to estimate or determine the wetting state of the system, such as contact angle test, and spontaneous imbibition test.In the Finally, it is decent to mention that although the obtained results in this work demonstrated the applicability of using the OEO setup for calculation of both capillary pressure, and relative permeability curves, however, it is still advantageous to experimentally verify the model and the related parameters in the future. Furthermore, the analysis of the features of the new setup in the imbibition flow processes is recommended in future studies.5•The equilibrium saturation profiles for both TEO and OEO installations follow the same distributions. So, similar capillary pressure curves can be obtained in both installations.•The dynamic trend of flow and distribution of fluids are different in the two setups. This difference is due to the different boundary conditions, where the TEO setup creates a co-current flow regime, while it is counter-current in the OEO setup.•nw phase has very low mobility at points close to the outer boundary  for the OEO system is higher than the time for the TEO system, especially in more water-wet cases. This is due to the definition of boundary conditions where the •nw viscosity (the invading phase) in changing the flow regime were more significant in comparison to the w phase.In the water-wet capillary pressure state, the dynamic saturation profile in the TEO setup is significantly influenced by the viscosity ratio of fluids, and more viscosity ratio created more frontal flow behavior. The impacts of •In the OEO setup, no sensitivities to the viscosity ratio (in either viscosity) were found in the water-wet system, however, a slight change in the dynamic saturation profile is observed at mixed-wet conditions.•The equilibration time of the flow is significantly influenced by the viscosity values that may lead to changes in the economic feasibility of centrifuge tests, especially in the OEO system. The time scale was more dependent on the viscosity of the most mobile phase.•nw mobility region close to the open boundary, that limits the flowing velocity of fluids.The presence of the threshold capillary pressure significantly rises the test time for the OEO setup by establishing a low •Considering the long temporal duration of performing the OEO centrifuge test in cores with water wet behavior, running OEO tests may be infeasible in these cores, especially in cases with high threshold capillary pressures. However, since we showed that the OEO model is almost insensitive to the viscosity of fluids, the OEO test can be run with different viscosity values. This approach can be used for reducing the test time of OEO setups.•This newly proposed setup should help to calculate the counter-current relative permeability by an inverse calculation method with reasonable accuracy and applicability in real conditions. It can be performed by history matching the production volume time series. Our investigations regarding the OEO setup showed that obtaining a unique relative permeability is guaranteed under different conditions.•The findings reported here also shed new light on the uncertainties in the inverse calculation of relative permeability by centrifuge setup. For the OEO setup, it was found that in the viscosity ratios far from 1 (M≪1 or M≫1), the relative permeability corresponding to the lower viscosity phase may show a higher level of errors.In this work, a numerical model for simulation of centrifugal capillary pressure test in two different boundary conditions  is provided. The developed model for the TEO setup was validated against the Sendra v2018.5 simulation software. Below points are concluded in this work:NomenclatureJassem Abbasi: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Pal Andersen: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.https://github.com/jcabbasi/centrifuge_public.Data associated with this study has been deposited at The authors declare no conflict of interest.No additional information is available for this paper.


Ecosystem-based management requires understanding of food webs. Consequently, assessment of food web status is mandatory according to the European Union’s Marine Strategy Framework Directive (MSFD) for EU Member States. However, how to best monitor and assess food webs in practise has proven a challenging question. Here, we review and assess the current status of food web indicators and food web models, and discuss whether the models can help addressing current shortcomings of indicator-based food web assessments, using the Baltic Sea as an example region. We show that although the MSFD food web assessment was designed to use food web indicators alone, they are currently poorly fit for the purpose, because they lack interconnectivity of trophic guilds. We then argue that the multiple food web models published for this region have a high potential to provide additional coherence to the definition of good environmental status, the evaluation of uncertainties, and estimates for unsampled indicator values, but we also identify current limitations that stand in the way of more formal implementation of this approach. We close with a discussion of which current models have the best capacity for this purpose in the Baltic Sea, and of the way forward towards the combination of measurable indicators and modelling approaches in food web assessments. The online version contains supplementary material available at 10.1007/s13280-021-01692-x. With increasing human use of the marine environment, an ecosystem-based approach to management of human activities is widely acknowledged as the fundamental principle to accomplish sustainable resource use and maintain healthy marine ecosystems . With food web indicators, we mean any data product that can be used to assess the state of a marine food web and that reflects impacts of human activities. In this perspective, we provide a comprehensive review and a gap analysis of the food web indicators developed for the Baltic Sea, which serves as a good example of a marine area with a well-established and long-running monitoring regime and is one of the most data-dense regions in the world. Many indicators have been operationalized, and knowledge of the food web interactions is on a comparatively good basis due to the relatively low marine diversity compared to oceanic ecosystems , the HELCOM indicator catalogue , as well as scientific publications of food web indicators. The scientific publications were available through multiple, properly documented international sources, including research papers in scientific journals, EU-funded project reports, and online publications by international organisations or research groups . Some of the indicators were closely similar, but we treated them separately if there were differences in the name or calculation method and the descriptions did not mention similarity to any other indicator.S1). This synthesis allowed us to then conduct a gap analysis of the suite of available indicators against the GES criteria , which is further divided into four GES criteria: the diversity within and balance of abundance between trophic guilds , as well as the two secondary GES criteria: the size distribution of individuals across a trophic guild (D4C3) and the productivity of the trophic guild , as well as the various indices comparing proportion of large fish to all the fish . All these cover two trophic levels, except the MSTS which attempts to cover three levels, determining status on the basis of phytoplankton abundance (as food for zooplankton) and on the basis of the condition of planktivorous fish (indicating zooplankton as food for fish)  is an essential indication of the state of food webs. It means that the biomass or abundance ratios of the different trophic guilds are not adversely affected, as might happen for example if the abundance of top predators decreases and that of their prey, therefore, increases. We found that 11 indicators include at least three guilds . As the search terms were, on purpose, general, we received > 250 hits among which we had to pick studies fitting to at least one of the GES criteria . Most of the models included the three most important commercially exploited fish species, sprat, herring, and cod which represent the ‘Planktivores: nekton (excl. warm-blooded)’ and ‘Sub-apex demersal predators: nekton (excl. warm-blooded)’ (Table S4).The models are mostly defined at species (sometimes genus or higher taxa) level. We mapped the species in each model to the trophic guilds according to ICES  and Ojav)’ Table . Other t)’ Table  and Atla)’ Table  models i)’ Table  the trop)’ Table , the groThe analysis Table  indicateS4). While all the existing models already include some of the key pressures on the Baltic Sea, most often fisheries and eutrophication, impacts of hazardous substances and non-indigenous species were missing in many models. The EwE and Atlantis models can be developed to include these pressures and evaluate their management  to fill in the indicator gaps can be evaluated through reviewing model predictions for ecosystem components whose data has not been included into the model, but for which some data are available so that the prediction accuracy can be evaluated  Ecopath—a static, mass-balanced snapshot of the system, (ii) Ecosim—a time dynamic simulation module and (iii) Ecospace—a spatial and temporal dynamic module . In the Baltic Sea, where the regional sea convention (HELCOM) is preparing for the next assessment of the state of the marine environment, we propose to select pilot assessment areas in order to test our approach and overcome the technical challenges of the integration. Despite the support from models, food web assessments should still rely on monitored data and transparent metrics as is the spirit of the policy assessments like the EU MSFD.Supplementary file1 (PDF 303 kb)Supplementary file2 (XLSX 49 kb)Below is the link to the electronic supplementary material.