Since Chinese households account for more than half of the country’s total carbon emissions, efforts focused on consumption will be key to reaching carbon reduction targets. The Individual Low-carbon Behavior Rewarding System (ILBRS) is an emerging mechanism in China that encourages the public to develop a low-carbon lifestyle and it is critical to look for various approaches to enhance the public’s willingness to participate in it. The framing effect has been widely used to study pro-environmental behavior as a low-cost nudge. We used an online questionnaire (N = 320) to investigate how framing information (loss and gain framing) influenced people’s willingness to participate in the ILBRS through the mediation of environmental risk perception. The results indicated that the public’s willingness to participate in the ILBRS under the loss frame was significantly higher than the gain frame. Furthermore, environmental risk perception played a mediating role in the proceedings. Based on our findings, the designers and promoters of ILBRS systems could employ loss-frame information to promote the public’s willingness to participate in the ILBRS and drive more people to live a low-carbon life in the process of mechanism construction, information communication, and operational promotion. As the world’s largest emitter of greenhouse gases, China is facing a considerable challenge . To reduMany ILBRS projects have achieved tremendous things in recent years. According to the related reports, Fuzhou City, Jiangxi Province, established an ILBRS platform in 2017. The number of registered users reached 400,000 in four years, distributing more than 73.71 million carbon coins and reducing carbon emissions by about 160,000 tons . In SeptNevertheless, it still fails to achieve the purpose of widely guiding the public to live a low-carbon life with low public participation. Due to the high operating costs and insufficient publicity, some ILBRS platforms have lost users and ceased operation. Existing studies generally discuss the system design and practical significance of the ILBRS, but few discuss how to increase public willingness to participate in the ILBRS from a psychological perspective ,13,14. TNudge is recognized as an effective tool that can coordinate behaviors and intentions to promote behavioral change. It has been used by several governments and organizations around the world to improve public policies on the environment, health, and education ,17,18,19Positive and negative framing measures (framing effects) are an essential nudge method. The framing effect was first introduced by Tversky (1981) and led to different decision judgments by makers through different descriptions of an identical problem . ExistinDifferent framing wordings elicit different emotional responses, and the emotion is an important factor influencing risk perception . Risk peIndividuals are the drivers of green transformation in society and a vital participating force in the carbon market. Many countries have explored individual energy-saving and emission reduction mechanisms, such as South Korea’s Green Card, Japan’s environmental point system, and China’s ILBRS . HoweverLow material incentives cannot fully motivate public participation, while long-term financial incentives increase the burden on the platform or the government. We, therefore, introduce a non-priced nudge approach—the framing effect—to investigate the facilitating effect of framing information on increasing public participation in the ILBRS. Tversky (1981) discovered and proposed the framing effect in a study on Asian disease problems. In the experiment, participants preferred to choose the risky option in the negative frame and the deterministic option in the positive frame. Tversky argued that differences in the form of expression caused different choices. Framing effects are typically classified as risky choice framing effects, feature framing effects, and goal framing effects ,30,32. IRegarding the impact of the gain/loss framing, current research has yielded mixed results. Some studies have shown that loss frames are more effective in influencing behavior. For example, loss frames have a more significant effect on promoting recycling behavior than gain frames . A compaH1: Individuals’ willingness to participate in the ILBRS in loss framing is significantly higher than in the gain framing.Risk perception refers to the individuals’ subjective feelings and perceptions about various objective risks in the external environment. It was initially introduced by Bauer (1960) in consumer behavior . Risk peH2: Loss framing significantly increases environmental risk perceptions compared to gain framing.H3: Environmental risk perception plays a mediating role in the framing’s effects on people’s willingness to participate in ILBRS, and the loss frame causes more environmental risk perception to increase the individuals’ intentions to engage in the ILBRS.According to previous studies, educational attainment, personal economic background, gender, and age influence environmental risk perceptions . TherefoThis study used a between-participants design to provide participants with different textual framings (loss framing description or gain framing description). Then we measured the participants’ willingness to participate in the ILBRS and environmental risk perceptions to explore the influence of loss and gain framing on the willingness to participate in the ILBRS and the mediating role of environmental risk perceptions. The process details were as follows: Participants received one of the two questionnaires in WeChat. They were required to read the introduction of the ILBRS under the gain framing (or loss framing) first, then the level of environmental risk perception was measured, and finally, they answered the questions on demographic-related variables. The reading time of the material was limited to at least 15 s to ensure the participants adequately read the preceding frame material.We used SPSS statistical software and the PROCESS program to organize and analyze the data. First, descriptive statistics were performed. An independent sample t-test was used to analyze the effect of the gain and loss framing on willingness to participate and environmental risk perceptions. Finally, the PROCESS program was used to mediate the data using the Bootstrap method.The material for introducing the ILBRS learns from the basic introduction of the ILBRS platforms such as Ant Forest and Carbon Planet. The descriptions of the gain and loss framings refer to the study by Spence (2010) . We descGain frame:Collecting 2280 g of carbon energy means we can protect 1 square meter of the primeval forest which is the home of animals.Collecting 10,800 g of carbon energy means we can protect 10 square meters of Sanjiangyuan Park which is the home of the Chinese snow leopardCollecting 20,000 g of carbon energy means that one sand willow can be planted in the desert to stop the spread of the Mongolian desert by 1 square meter2 and slow down climate change.Collecting 50,000 g of carbon energy means that one camphor pine can be planted to help absorb 50 g/day of COCollecting 80,000 g of carbon energy means we can plant an elm tree, stopping 1 square meter of arable land in the Yellow River basin from being washed away.Loss frame:Without collecting 2280 g of carbon energy, 1 square meter of virgin forest is destroyed without protection and animals lose their homes.Without collecting 10,800 g of carbon energy, 10 square meters of the Sanjiangyuan Park, home of the Chinese snow leopard, are destroyed due to lack of protection.Without collecting 20,000 g of carbon energy, 1 square meter of the Mongolian desert spreads forward due to the lack of a sand willow.Without collecting 50,000 g of carbon energy, the carbon dioxide we produce today worsens the climate as a result of the lack of a single camphor pine to absorb it.Without collecting 80,000 g of carbon energy, 1 square meter of arable land in the Yellow River basin is washed away by heavy floods due to the lack of a single elm tree to hold it in place.Questions on demographic variables: including gender, age, education, profession, city of residence.Willingness to participate in the ILBRS: according to the acceptance model, the ILBRS willingness to participate scale refers to Saari’s (2021) behavioral willingness scale , which uEnvironmental risk perceptions: the scale of environmental risk perceptions was chosen from the study of Leiserowita (2006) . A questt = −3.103, p = 0.002). The person’s perception of environmental risk was significantly higher in the loss frame than in the gain frame . These results validate hypotheses 1 and 2 of this study.In this study, questionnaires were distributed through the Internet, and finally 320 valid questionnaires were collected, including 156 in the gain frame. The results of comparing people’s willingness to participate in the ILBRS and environmental risk perceptions under loss and gain framing by independent sample t-test are shown in ps > 0.05). Controlling these variables, we found that the framing still significantly (p = 0.021) influenced the dependent variable. The results of the hierarchical regression analysis show that, after controlling for the effect of demographic variables, the independent variable still significantly affected the dependent variable and the main effect was still significant.In order to further verify whether the frames significantly affect people’s willingness to participate in the ILBRS, and excluding the effect of other demographic variables on the main effect, a series of multiple regression analyses was conducted. In step 1, we put the demographic variables into the model using the enter method, including gender, age, education level, and monthly living expenses. Based on step 1, we put the independent variable, the frame, into the model in step 2. We obtained the results shown in A bias-corrected non-parametric percentile bootstrap method was used in SPSS in the process macro-program to further investigate the relationship between the framing, environmental risk perception, and willingness to participate. Moreover, 5000 self-sampling tests were conducted to examine the mediation effect of the environmental risk perception in the framing effect on the willingness to participate in the ILBRS based on the mediation effect test method proposed by Zhonglin Wen et al. (2004) according to the research hypothesis . The resCombining the results of the t-test and regression analysis, we can learn that the loss frame significantly increased the environment risk perception and thus increased the individuals’ intention to participate in the ILBRS, where the environment risk perception played a mediating role. These results validate hypotheses 3 of this study.The ILBRS, a vital tool to reduce carbon emissions from the consumer end, can raise the public’s awareness of reducing carbon and environmental protection and guide them in developing low-carbon and green living habits. Many studies explore the mechanism of the ILBRS, but few studies pay attention to the willingness to promote the use of the ILBRS. This paper investigates the effect of framing on the willingness to participate in the ILBRS through an experimental questionnaire. Conducting an independent sample t-test on the resultant data, we concluded that the willingness to participate under loss framing is significantly higher than that under gain framing, which is consistent with hypothesis H1. This indicates that different linguistic descriptions of the framing show different degrees of impact on the willingness to participate in the ILBRS. People are more sensitive to loss framing. This point validates the prospect theory that people are more sensitive to losses than gains when making decisions. This phenomenon may be related to people’s loss aversion, where a certain amount of loss brings more utility reduction than the same amount of gain brings utility increase.Furthermore, it has been previously shown that perceived risk promotes people to take action when they believe they are capable of acting and can successfully avoid or mitigate adverse outcomes . The ILBMoreover, according to previous studies, the framing effect is influenced by different factors, such as emotions, information sources, and risk perceptions . ThroughFirstly, this paper builds on a psychological perspective to explore how to promote public willingness to participate in the ILBRS. Most studies on the ILBRS nowadays focus on institutional design and other content but rarely explore how to promote public participation from a psychological perspective. This study explores the factors influencing the promotion of public participation in the ILBRS by introducing the boosting framing and finds that loss framing is more effective than gain framing in guiding the public’s willingness to participate in the ILBRS. The results support the prospect theory that the gain and loss frame determine how people perceive information. Because of loss aversion, public willingness to participate in the ILBRS is significantly higher under the loss frame than under the gain frame. Second, our study confirms the critical role of environmental risk perception variables in the ILBRS related research, providing a reference for future research in this area. This study explores the mediating role of environmental risk perceptions in the framing effect on the willingness to participate in the ILBRS. It shows that environmental risk perceptions partially mediate the effect of framing on the willingness to participate in the ILBRS. The indirect effect of 48.56% shows that environmental risk perceptions are an essential mediator of the framing effect on the willingness to participate in the ILBRS. Therefore, subsequent studies can continue to explore the application of the framing effect in environmental policy from the view of risk perception.From the policy perspective, this study provides new ideas for optimizing and promoting the ILBRS. First, the current ILBRS is mainly promoted with positive message frames, while our study shows that using loss frames in the ILBRS is more effective in increasing public willingness to participate. According to the research in this paper, the ILBRS can be promoted with more negative information in public settings, and the program design of the ILBRS can also emphasize more negative framing in the frame building and feedback mechanism, etc. Second, the ILBRS currently uses economic incentives to attract public participation. This study provides non-economic incentives, nudge, and demonstrates that nudge positively impacts the willingness to participate in the ILBRS. Some researchers suggest that the best results can be achieved by using multiple boosting methods in one setting ,51. ThusAlthough this study has experimentally demonstrated the framing’s effectiveness on the public’s willingness to participate in the ILBRS, there are still some limitations. On the one hand, this study used an online questionnaire to explore the public’s willingness to participate in the ILBRS. It is a complex process from behavioral willingness to taking action. The one who has a high willingness may not necessarily take actual action. On the other hand, the sample in this study was made up of a group of college students, which cannot represent all the objects of the ILBRS. A more diversified sample should be selected in the future to verify its general effect.At present, more theoretical research and practical innovation are needed to explore the psychological mechanism behind public support for the ILBRS and to explore the innovation of the ILBRS incentive mechanism and publicity content, which will provide more practical ideas for the design of the ILBRS in the future. Therefore, we have several suggestions for future research directions. First, researchers should explore long-term nudge methods because energy saving and carbon emission reduction are ongoing tasks that require constant public participation. Second, researchers should explore more effective nudge tools to help attract more people to participate in the ILBRS. Third, researchers should investigate the influencing factors of public participation in the ILBRS through field experiments. In addition, future researchers should expand the experimental sample to include all social classes and age groups, so that more comprehensive and integrated findings can be obtained. Fourth, researchers can conduct a more comprehensive study on the mechanisms underlying the influence of the framing on individuals’ willingness to participate in the ILBRS. In this study, environmental risk perception failed to play a complete mediating role, suggesting the potential of other influence factors.This study investigates the effect of different framings on the willingness of the public to participate in the ILBRS and the role of environmental risk perceptions in it. The results show that willingness to participate in the ILBRS is significantly higher in loss framing than in the gain framing. The perception of environmental risk plays a partially mediating role. Loss framing can influence people’s willingness to participate in the ILBRS by affecting their perception of environmental risk.Overall, this paper enriches the application of framing effects in the environmental protection field, expands the research to the mechanism of framing effects, and provides a theoretical basis and new ideas for the ILBRS in terms of content construction and policy promotion.


Up to now, fiber-reinforced composites with thermoplastic matrix have seen limited fields of use in the structural scope due to their high viscosity in the molten state, which results in poor impregnability of the reinforcement, leading to mechanical properties of the finished product that are not comparable to those of thermosets. Although the latter still dominate the various sectors of automotive, aerospace, transportation and construction, new applications involving the production of thermoplastic composites are growing rapidly, offering new approaches to the solution of this problem. The aim of this work is to study and evaluate the state of the art on the manufacturing processes of thermoplastic matrix composite, analyzing the parameters that come into play and that most influence the process and material performance. The advantages of film stacking and powder impregnation techniques are contrasted by the versatility of hybrid fabrics and, at the same time, parameters such as pressure and temperature must be carefully considered. A description of different thermoplastic composite processes such as powder impregnation, film stacking molding, hybrid woven fabric, hybrid yarn and products follows, which represent the current possibilities to move from a thermosetting matrix composite to a thermoplastic one, upon which the concept of sustainability is based. This article wants to present an overview of research that has been done in manufacturing thermoplastic reinforced composites and will serve as a baseline and aid for further research and development efforts. Thermoplastic composites (TPCs) offer some advantages over thermosets, such as higher toughness, faster production and, first of all, their recyclable nature . Many noNevertheless, one of the most effective solutions lies precisely in their production, in order to move toward eco-sustainable and circular composites. The strong commitment of world organizations in the field of safeguarding the planet has directed the research of these materials toward production processes with a lower environmental impact and a strong propensity to recycle the polymeric part . The devThe rapid increase in the application of fiber-reinforced polymer matrix composites is creating a challenge for waste recycling. The use of high-performance thermosetting polymers as a matrix makes the recovery of fibers and resins extremely difficult. Implementing a circular economy able to eliminate waste and reuse resources ensures the use of efficient processes to recycle thermoset composites components and manufacturing wastes .Definitely, the outstanding performance of conventional thermosets arising from their covalently cross-linked networks directly results in a limited recyclability. The available commercial or close-to-commercial techniques facing this challenge can be divided into mechanical, thermal and chemical processing. However, these methods typically require a high energy input and do not take the recycling of thermoset matrix itself into account. Rather, they focus on retrieving the more valuable fibers, fillers, or substrates . This puIt is a fact that, when taking into account factors such as climate change, global warming, environmental sustainability and circular economy, the landfill or incineration of thermoset component wastes must be avoided. In this context, more efforts are required to improve the technology readiness level of the processes in place and their scalability should be economically accessed. To develop commercially viable recycling activities, the future research studies must be focused on the following points: achievement of consistent quality of recycled fibers; reuse of them as reinforcement in thermoplastic polymers, also from renewable sources; study of mechanical properties after reuse and remanufactured technologies evaluation of the potential to close the life cycle loop of CFRPs and reducing energy consumption and recycling cost. In this field, innovation is multifaceted and can come both from the constituent materials  and production processes .This review will be focused on the latest advances and development of reinforced thermoplastic, mainly referring to continuous fibers and non-woven fabric composites and related processing technologies: relationships between impregnation mechanisms, consolidation quality and resulting mechanical properties of composites manufactured from film stacking, commingles fabrics and dry powder impregnation systems will be investigated. This paper highlights how these materials can be processed, adopting a completely different paradigm in comparison to that of the thermosetting matrix, the former not requiring a reaction to set the properties but complex thermal cycles, often performed in a short time span.It is possible to make an important distinction between various impregnation methods: we distinguish those that precede it, called pre-impregnation, and those that follow it, called post-impregnation. In the former, the fibers impregnation takes place in a single step using a polymer in the molten state. The various layers thus obtained are stacked and subsequently consolidated due to the application of heat and pressure. In the latter, however, it is expected that the impregnation occurs during the processing of the piece starting from a polymer in the shape of a film, powder or filament . In thisFilm stacking, technique that consists in alternating thin polymer sheets with the reinforcement and, subsequently, compacting them;Powder impregnation technique consists in covering and, therefore, impregnating the fiber fabric with the polymeric matrix in the shape of a dry powder ;Hybrid woven fabrics;Hybrid yarns.Heating of the press to lower the matrix viscosity;Increasing of the pressure to force the liquid-state thermoplastic matrix to impregnate the fabric;Cooling of the press to solidify the laminate.Film stacking consists in heating and compressing a sequence of alternated layers of thermoplastic films and dry fabric reinforcements . The resTemperature, pressure, and holding time are the core parameters to control the process. Their values depend on the nature of the polymer, mainly melting temperature and viscosity, as well as on the fabric tortuosity and the load and temperature the fibers can sustain . Below aProcessing temperature considerably affects the mechanical and microstructural properties of the composite produced. It is known that the use of thermoplastic resins introduces the issue of adequate fiber bed impregnation because of the higher viscosity of thermoplastic melts than thermosetting ones. Optimization in processing temperature is crucial. High temperature pressing scheme promote a lower resin’s viscosity and therefore greater mobility and fluidity to penetrate between the fibers and impregnate them. On the other hand, the use of high temperatures potentially clashes with the thermal stability of the materials constituting the composite. Then, an optimal condition will exist where adequate resin flow occurs during consolidation  and thermal degradation of the constituents is kept at a minimum .Hu et al.  exploredThe pressure drives the thermoplastic resin to infiltrate into the compliant fabric while simultaneously deforming it, altering the infiltration kinetics . This paSuresh and Kumar  researchOther than temperature and pressure, the application time for these parameters is another critical consideration in the film stacking process. Insufficient holding time could compromise the laminate’s microstructural quality, producing defects on the sample, which are associated with inadequate pressure and temperature .As with processing pressure, Katayama et al.  investigYang et al.  analyzedThe use of thermoplastic composites has been limited by the lack of reliable techniques to make prepregs of consistent quality. Dry powder impregnation methods offer a new approach to the solution of this problem. This process was identified as the technique with the greatest potential for success as a valid alternative to produce prepreg thermoplastic tapes . VariousFiber tow spreading;Particulate flow pattern;Particle size;Dielectric properties of fibers and particles;Particle cloud concentration ,37.These processes differ mainly in the way in which the particles of the matrix are deposited on the fibers and in the forces of particle/fiber interaction responsible for their adhesion. In most dry processes, the fibers are passed through an air suspension of particles. Some of the main factors that influence the mode and rate of deposition of the powder are:It is expected that the powder is introduced into a fiber tow which is processed by heating to sinter the particles onto the fibers. Price  was the Powder polymer matrix and reinforcement can contact each other electrostatically. The impregnation takes place thanks to the charged powder particles deposited on the fabric surface ,39,41. ITape manufacture, which involves the fiber tows impregnation with thermoplastic particles and their subsequent coalescence on the fibers to form a flexible prepreg tape;Consolidation, which involves laying prepreg tapes into a mold followed by heat and pressure application to form void-free composites. An important aspect for optimizing the processing cycle is the characterization of the different properties of the thermoplastic matrix and the reinforcing fiber.The flowchart for the manufacture of powder-impregnated thermoplastic composites is shown in It would be little affected of the viscosity of the matrix. Most high-performance thermoplastic matrices are highly viscous (104 to 105 Poise) above their softening point (amorphous) or melting point . A good dry powder process would circumvent this problem by coating the fibers individually so that flow occurs over very short distances of the order of microns;It would avoid the use of binders, solvents or water that must be evaporated during the last stages of the processing cycle. Incomplete removal can result in voids which have a detrimental effect on the mechanical properties of the composite;The introduction of secondary material processing operations such as fiber spinning which could increase the cost of the final product would be avoided .Dry powder processes have numerous advantages for the production of thermoplastic prepreg tape. An ideal process would have the following advantages over other processes:g can be defined to indicate the interlacing counts between the fill (polymer) and weft (reinforcement) yarns. The ng index is equals two for plain weave, three for twill weave, and four or greater for satin weave [Hybrid woven fabrics (HWFs) are characterized by orthogonal interlacing of two sets of yarns: polymer filaments and reinforcing long fibers in different architectures  constituting the fill (warp) and weft, respectively. There are many types of weaving patterns employed to create HWFs: plain weave, twill, and satin . Accordiin weave .Irrespective of the type of polymer matrix and reinforcing fibers, the weave pattern used for making the HWF contributes to the mechanical properties of the composite. For instance, research conducted by Alavudeen et al.  on naturCompared with the common unidirectional laminates, HWF composites provide better dimensional stability over a large temperature range, more balanced properties in the fabric plane and the interlacing of yarns results in higher out-of-plane strength, better impact resistance, and tolerance . In the On the best authors’ knowledge, recent literature regarding optimization of the manufacturing process of hybrid fabric as well as the effect of thermo-forming parameters on the final composite’s characteristics is very limited. Below, some topical studies are reviewed.Lin et al.  combinedw/w%) and PP fibers (40 w/w%)  . The comDi Benedetto et al.  developeThe purpose of this study was to optimize the energy absorption properties of composite and then increase their crashworthiness for using on automotive components. Five consolidation temperature  and two pressure conditions (<0.3 MPa and 0.3 MPa) were investigated to build the predictive model.Processing temperatures above 260 °C negatively affected the dynamic mechanical response of the composite due to thermos-oxidative degradation phenomena that the polymer matrix underwent.Indeed, in accordance with the thermal degradation kinetics method implemented by the authors, the material crashworthiness is highly associated with the degradation kinetics. The composite is more resistant when the degradation rates are reduced. With regard to the pressure, the laminates manufactured at 0.3 MPa provided a homogeneous and defects-free microstructure a comparev/v% Jute–60 v/v% PP) HWFs as preforms for bio-composite laminates fabrication. The woven architecture and the cross-sectional optical micrographs of the laminate obtained by compression moulding  are reported in Souza et al.’s research work  proposedThe HWF technique was successfully applied in the manufacturing of Jute/PP hybrid bio-composites by compression moulding. The obtained composites presented moderate mechanical properties of tensile strength (44.62 MPa) and elastic modulus (7.1 GPa), which were slightly better than the values found in literature on non-continuous fiber Jute/PP composites with same fiber/matrix proportion ratio. These performances suggest their possible application in non-structural components for reducing weight, costs, and environmental impact. The HWF has also presented interesting properties of malleability and strength required by textile industries for weaving processes.The use of hybrid yarns is one of the most promising routes for producing structural thermoplastic composites due to a low-cost manufacturing of complex shaped parts. This process can offer a very good distribution of matrix and reinforcement in a non-molten state before processing. To take advantages of the processing and fabrication reductions that can be provided by textile technology, the thermoplastic matrix should be incorporated into the yarns before their conversion into preforms. In hybrid yarns, indeed, matrix and fabric are blended intimately at the filament level, such as the polymer flow distance for impregnation is reduced when matrix and fibers are commingled each other. To obtain an excellent and rapid impregnation, it is important that reinforcement and matrix are in direct contact as much as possible and distributed in a homogeneous way, reducing the distance that the molten matrix must take to wrap the reinforcement. . To beneThere are many methods by which the hybrid yarns can be manufactured such as co-wrapping and commingling. These techniques involve bringing the matrix and reinforcement into close contact with the aim not only to obtain, as already mentioned, a uniform distribution but also to limit the detrimental effects.M is the polymer filament width (mm) and TM is the polymer wrapping turns per meter (turns/m).The yarns made by the co-wrapping method, called co-wrapped yarns, have a feature in which the reinforcing fibers are intimately combined with the thermoplastic matrix fibers using a self-assembled winding apparatus . ReferriWith respect to other hybrid yarn production methods, such as commingling process, co-wrapping ensures better protection for the reinforcing fibers during manufacturing. However, the distribution of the reinforcing fibers and the matrix fibers is still a challenge, requiring higher processing temperature and pressure to improve impregnation . As for In addition, the intermediate value of linear density (50.67) was discovered as optimal in terms of mechanical properties, providing the best condition for the radial compressive force exerted by the wrapped filament to interact with glass core. At the optimal process conditions, co-wrapped yarns provided breaking load and tenacity 62% and 46% higher than those of the neat glass fibers (not wrapped with polyester). Baghaei et al.  studied This is to be attributed to the higher thermal conductivity of carbon fiber (~1.31 W/m × K) compared to glass (~0.25 W/m × K) which would sanction a more effective heat transfer at the fiber–matrix interface and therefore not requiring higher temperatures to drive the melting of the matrix and the consequent impregnation of the reinforcement .Commingling is one of the most promising techniques founded on the principle of uniform distribution of continuous matrix and reinforcement filaments during melt spinning . The homDue to the commingling process, both reinforcing and polymer fibers can be combined, representing great potential ,68. The DG and VTG represents, respectively, the speed of delivery godet and the take-off godet [Nevertheless, the process involves technological challenges concerning the impregnation as well as wetting of the fibers hence a decrease in the quality of the consolidation . The atmff godet . Numerouff godet ,72. Jumaff godet  reportedff godet  examinedff godet  reportedff godet  analyzedff godet  investigff godet ,78 has fff godet , as showff godet . The conff godet . Specififf godet . In theiCompared to the production processes analyzed so far, the hybrid yarns technique is certainly the most ambitious and promising since they have some non-negligible advantages such as high flexibility, and this allows to obtain more complex parts . CommingThermoplastic composites can play a key role in 21st century industry, as new materials are emerging day by day, while manufacturing processes are evolving to meet the stringent industrial performance requirements, production and multifunctionality. This section aims to summarize and highlight the key aspects of each technique, the various advantages, disadvantages and the parameters that most influence and drive processes choices that cannot be ignored. Film stacking molding is widely used due to the relative ease of manufacture. Disadvantages include high resin content, the uneconomic nature of the process, and the difficulty in impregnating the fiber tow (high pressure forces the fibers together) with high-viscosity matrix material. We have seen how important processing temperature, pressure and holding time are, as the core parameters to control the process. The higher the temperature, the lower the resin viscosity, an advantage that guarantees better mobility and fluidity through the fiber bed. On the other hand, however, high temperatures can degrade the material, nullifying its contribution. Pressure represents a crucial parameter: if too low, the impregnation time increases with the consequence to obtain dry spot inside the composite. Nevertheless, an excessive increase would cause a reduction in the fabric permeability. In the powder impregnation process sufficient pressure and time must be applied to force the resin to impregnate the fiber tows in order to achieve fully consolidated composite parts. Poor impregnation and consolidation will result in a significant reduction in the mechanical properties of the final composites . We repoAn aspect of extreme importance lies in the fact that these are industrial production processes and that allows to start from thermoplastics obtained from recycled material.This review article investigated the main production technologies to produce thermoplastic matrix composite materials. The reason lies in a principle that is now more important than ever, sustainability: the opposite nature of thermoplastic matrix composites compared to thermosets, i.e., the possibility to recycle them. The thermosetting polymer certainly guarantees the best mechanical characteristics but it can no longer be reworked and reused for other purposes. Conversely, thermoplastic polymer can be remelted and subjected to a new type of processing. The goal is to produce high-performance thermoplastic matrix composite materials, with high volume-based quantities of reinforcement, to allow a real comparison with thermosetting materials. There are two process techniques to manufacture thermoplastic composite parts: reactive and melt processing. The first solution focuses on in-situ polymerization, starting from water-like viscosity monomers that could potentially provide an optimal reinforcement impregnation, and a subsequent polymerization reaction triggered by temperature increase . The sec


The essential factor that enables medical patient monitoring is the vital signs monitor, whereas the key in communication with the monitor is the user interface. The way the data display on the monitors is standard, and it is often not changed; however, vital signs monitors are now configurable. Not all the data have to be displayed concurrently; not all data are necessary at a given moment. There arises a question: is the standard monitor configuration sufficient, or can it lead to mistakes related to delays in perceiving parameter changes? Some researchers argue that mistakes in life-saving activities is not mainly due to medical mistakes but due to poorly designed patient life monitor interfaces, among other reasons. In addition, it should be emphasized that the activity that saves the patient’s life is accompanied by stress, which is often caused by the chaos occurring in the hospital emergency department. This raises the following question: is the standard user interface, which they are used to, still effective under stress conditions? Therefore, our primary consideration is the measure of reaction speed of medical staff, which means the perception of the changes of vital signs on the patient’s monitor, for stress and stressless situations. The paper attempts to test the thesis of the importance of the medical interface and its relation to medical mistakes, extending it with knowledge about the difference in speed of making decisions by the medical staff with regard to the stress stimulus. The graphic user interface is now an inseparable element of the work of almost every human being. Paying particular attention to the characteristics of people’s work in the medical industry, it is noticeable that their work, especially of doctors and nurses, often occurs in the general chaos caused by a wide variety of emergencies connected with their patients. This increases the level of fatigue and stress in physicians.One of the essential tools used by medical staff is patient life monitors, which started to be common in the 1980s , or at lThe monitors noted above are graphical interfaces designed to display vital signs. Unfortunately, to this day, patient monitors are not perfect, so not every change in patient parameters is immediately noticed . AccordiTherefore, it is crucial that medical staff work must be supported by the interface, not hindered. It is worth noting that, currently, vital signs patient’s monitors are configurable . Not allAs stress and fatigue are integral components of the work of medics, the medical graphical user interfaces useful in a neutral (stressless) situation may not be compatible in rapid and emergency situations when users’ reactions can be fairly different. The problem is presented in Hypothesis: The visual detection of critical parameters under stress is less noticeable than during stress-free situations. Thus, the main goal of our research was to check the reaction of medical staff in a stressful situation related to the perception of the patient’s vital parameters. To achieve our aim, we designed the user study with medical simulators to verify the given hypothesis precisely with a virtual patient simulator and its life monitor.This article attempts to examine medical staff’s reactions in a stressful situation when working with the patient’s life monitor and answer the following question: is the standard monitor configuration sufficient, or can it lead to mistakes related to delays in perceiving parameters changes? Therefore we formulate the following Our research is based primarily on the analysis of the medics’ eyes movements, i.e., which places of the patient’s life monitor are under the interest of medics in a stressful and stress-free situation. In order to be sure that the stimuli that the participants were subjected to indeed caused stress, we analyzed not only the subject’s eye movement with eye-tracker  but also the heart rate record with a wearable chest belt for heart monitoring . As both ECG signal metrics (such as HR (ECG) and LF/HF (ECG)) and ET (such as pupil size and time of fixation) identify a stressful situation, they have been used in our analysis.Measuring medical staff reactions related to perception of vital signs on patient’s monitor under stress and stress-free situations;Confirming the efficiency of the stressors used in the experiment  through the metrics computed from acquired ECG and ET signals;Drawing attention to the problem related to the current functional layout of the patient’s graphical interfaces.The main contributions of our work are:In order to achieve our research goal, an experiment was conducted. We describe it in The effectiveness and reliability of an employee are primarily influenced by his/her adaptation to the working conditions. However, this adaptation is mainly possible without negative factors that make work difficult . The proAn important aspect is also that, since the early 1980s, numerous agencies and societies have addressed patient safety in medicine, e.g., Anaesthesia Patient Safety Foundation, Emergency Medicine Patient Safety Foundation, or National Patient Safety Agency . AnaesthEnrico Coiera, in , claimedThe issue of problems with medical interfaces is addressed more widely in the literature. In , the autMost of the publications on the problem of improper medical interface design mainly focused on propositions of solutions based on changes in the information architecture. According to a report , all phyIt is worth noting that mental overload makes people begin to behave less rationally  , which cThe experiment aimed to test the medical staff’s time reaction time to the falling vital signs of the patient displayed on the patient’s life monitor, both in a stressful and stress-free situation. The experiment was carried out with 21 respondents who declared normal or corrected to normal vision. All analyzed data were fully anonymized. Before the experiment, the participants gave their informed written consent to use the data from the perceptual experiment in the research (according to the Bioethics Committee Agreement no KB-0012/24/2020).As the experiment was performed using the medical vital sign monitor, eye tracker, and wearable ECG, the task of the respondents was to connect the patient to medical equipment and then to monitor the patient’s vital signs. Before starting the experiment, the respondent was informed that in a case of falling the vital signs of the patient she/he was obliged to undertake an appropriate medical procedure aimed at saving the patient’s life. The person responsible for the control of the proper experiment conduct had the possibility to change the patients’ vital parameters, i.e., decreasing them in a given time.Test stand. For the experiment we used the test stand located in the Medical Simulation Center of Collegium Medicum of the University of Zielona Gora . After the calibration process, the main experiment began. The experimental procedure consisted of the four following steps (see teps see :Step 1. Biosensors (ET and ECG) calibration. In the case of eye-tracker glasses according to the instruction [truction , only onStep 2. After the subject reported that he was ready to perform the experiment, the subject’s first task was to connect the patient to medical devices . Step 2 was the same for Experiment run 1 and Experiment run 2.Step 3. Monitoring the patient’s vital signs. In the step, we interfere in the vital sign level and the way they were displayed. Step 3 had different construction for Experiment run 1 and Experiment run 2.Experiment run 1. In this step, the medical staff was tasked with monitoring the patient’s vital signs. After 105 s, saturation went from 100% down continuously for 30 s.Experiment run 2. In this step, the medical staff was tasked with monitoring the patient’s vital signs. After 100 s, the random stressor  was applied. The procedure aimed to induce stress in the subject. Five seconds after the stressor was turned on, the saturation went from 100% down continuously for 30 s. The stressor was turned off after 25 s from switching on, in order to calm the subject. During the falling vital signs of the patient and the triggering stimulus, the subject was obliged to undertake an appropriate medical procedure aimed at saving the patient’s life.Step 4. When Experiment run 1 and Experiment run 2 were finished, the results from both experiments were analyzed for subject behavior, together with ET and ECG signals.Metrics for stress recognition. The topic of stress detection has been reported in different studies. The most often analyzed signals for stress identification were eye-tracking and ECG signals. For ET signal, pupil diameter and mean fixation duration are the most frequently used metrics [Mean fixation duration (ET) measures how long a person’s attention was focused on the scene. The higher the stress, the shorter the mean fixation duration. The eye movement becomes more chaotic [ chaotic .Pupil diameter (ET) is a common characteristic used in stress detection [etection . If an ietection ,26, the etection ,27,28,29HR (ECG) is a metric that measures heart rate variability, where the time between heartbeats varies slightly. Overall activity is more intense under stressful conditions [nditions ,31,32,33LF/HF (ECG)—Low frequency to high frequency ratio. It is an indicator of the autonomic nervous system balance between sympathetic to parasympathetic parts [ic parts ,36,37,38 metrics . For the metrics . Accordi metrics , these mp-value < 0.001 for all parameters. The analysis confirmed the consistency of the results and the stress response to the applied stressors. Change of pupil diameter, fixation time, heart rate, and LF/HF metrics are depicted in The following section discusses results from the perceptual experiment to find the influence of the induced stress on the time of medical staff reaction during a patient’s life parameter monitoring. To assure that the stimulus used in the experiment induced a stress reaction, we computed the metrics from ET and ECG signals. To make sure that the stimulus used in the experiment caused the stress response, we plotted the raw data (notation: HR (ECG), LF/HF (ECG), Pupil diameter (ET), and Mean fixation duration (ET)) to assess the changes taking place on the charts visually. We then examined the statistical significance to check whether the responses indicated by the metrics differed statistically significantly between stressful and stress-free situations. The results are presented in The next step was the analysis of the time of medical staff reactions to decreasing patient life parameters noticed in a stressless situation and under the induced stress. The difference is computed based on the eye-tracker data. To identify the moment when the respondent noticed the decreased saturation parameter, we defined the area of interest (AOI) when the parameter was displayed see . Visualip = 0.001. The Kruskal–Wallis test analysis results significantly differ between analyzed reactions  for time reaction on decreasing patient life parameters in stressless and stress situations (with the stimulus that induced the stress) is equal to ions see .p-value < 0.001). We also found clear signs that the participants’ stress-related behavior, i.e., increased pupil diameters, shortened fixation times, and heart rates, were consistent and significantly differed between situations with and without stressors .We have tested the hypothesis concerning the influence of stressors on the time of medical staff reaction on changing of a patient’s vital parameters. The hypothesis stated that stressors applied during simple clinical procedures concerning monitoring of vital signs will interrupt the correct actions of the research respondent. To receive reliable results, we ensured that the medical staff who took part in the experiment had similar medical backgrounds and experiences. We did not find any outliers in the subjects’ reactions. The results were entirely consistent, which made the results subject-independent. For the statistical analysis, we observed statistically significant prolonged response times of clinical decisions  were already impaired by caregivers’ responses in a stressful environment. More complex medical procedures are likely to cause even more errors or delays in performance. In addition, the results of our research can be used as an introduction to further analyses concerning the identification of sensitive areas on the screen, where information is poorly perceived by users. Moreover, they can be confronted with cognitive properties of participants. This is our focus for future research.The study results presented in the paper were the analysis of medical staff behavior during simple clinical procedure monitoring of vital signs that were interrupted with a stress-inducing stimulus, such as phone bell or the signal of an ambulance.The significant difference between reaction time in stressless and stressful situations was measured during the user study that was performed with medical simulations. We observed prolonged response times of clinical decisions and clear signs of the participants’ stress-related behavior.By studying both situations (stressless and stressful), we can formulate the statement that even the best-designed user interfaces, tested in a laboratory environment, i.e., without introducing the exhausting and annoying stimuli together, can be worthless. Human behavior, especially during stress and fatigue conditions, is determined by different factors. In our opinion, one of the most promising approaches can be the analysis of cognitive styles of subjects that describes natural human behavior, independent of his/her ability and knowledge.


Automated guided vehicles are widely used in warehousing environments for automated pallet handling, which is one of the fundamental parts to construct intelligent logistics systems. Pallet detection is a critical technology for automated guided vehicles, which directly affects production efficiency. A novel pallet detection method for automated guided vehicles based on point cloud data is proposed, which consists of five modules including point cloud preprocessing, key point extraction, feature description, surface matching and point cloud registration. The proposed method combines the color with the geometric features of the pallet point cloud and constructs a new Adaptive Color Fast Point Feature Histogram (ACFPFH) feature descriptor by selecting the optimal neighborhood adaptively. In addition, a new surface matching method called the Bidirectional Nearest Neighbor Distance Ratio-Approximate Congruent Triangle Neighborhood (BNNDR-ACTN) is proposed. The proposed method overcomes the problems of current methods such as low efficiency, poor robustness, random parameter selection, and being time-consuming. To verify the performance, the proposed method is compared with the traditional and modified Iterative Closest Point (ICP) methods in two real-world cases. The results show that the Root Mean Square Error (RMSE) is reduced to 0.009 and the running time is reduced to 0.989 s, which demonstrates that the proposed method has faster registration speed while maintaining higher registration accuracy. Under the background of “Industry 4.0”, the logistics industry is facing challenges, including structural adjustment, industrial optimization, cost reduction and efficiency improvement, and also has ushered in development opportunities such as information technology, intelligent logistics and machine vision . As an iThe existing vision-based object detection methods are mainly divided into two categories: the image-based method  and the With the rapid development of low-cost depth sensors, object detection has converted from traditional single point and segment measurement to dense point cloud and full profile measurement ,16,17. CIn response to the above problems, a novel pallet detection method for automated guided vehicles based on point cloud data is proposed, including point cloud preprocessing, key point extraction, feature description, surface matching and point cloud registration. The main contributions can be summarized as: (1) the proposed method considers the HSV color feature, which improves the detection accuracy; (2) an ACFPFH feature descriptor is proposed and the criteria for adaptive selection of the optimal neighborhood radius are established; (3) a new surface matching method called the Bidirectional Nearest Neighbor Distance Ratio-Approximate Congruent Triangle Neighborhood (BNNDR-ACTN) is proposed, which increases the efficiency and accuracy. The proposed method not only overcomes the drawback of randomness and inefficiency of neighborhood selection in traditional feature extraction but also improves the accuracy and efficiency of pallet detection. Moreover, the proposed method can be well adapted to a variety of complex scenes such as the ground and the shelf.The rest of the paper is organized as follows: In This section describes an overview of the proposed method. It consists of five modules: point cloud preprocessing, key point extraction, feature description, surface matching and point cloud registration. The framework of the proposed method is shown in Step 1: Point cloud preprocessing. The Percipio FM851-E2 3D vision sensor is used to acquire the point cloud data which represent the whole scene, including the pallet. Outliers are eliminated and the redundant information, such as walls and grounds, is removed using Random Sample Consensus (RANSAC) algorithm.Step 2: Key point detection. The key points with rich information are extracted from scene point clouds by the Intrinsic Shape Signatures (ISS) algorithm.Step 3: Feature description. The optimal neighborhood radius of each point is obtained based on the minimum criterion of the neighborhood feature entropy function. The color components and the geometric information based the optimal neighborhood of the key point are encoded into a representative feature descriptor called the Adaptive Color Fast Point Feature Histogram. The pallet template point cloud and its corresponding library of feature descriptors are obtained by performing the above three steps.Step 4: Surface matching. The matching method based on the Bidirectional Nearest Neighbor Distance Ratio (BNNDR) is employed to complete feature matching between the pallet template point cloud and the scene point cloud. Considering that there are some incorrect matching point pairs which will reduce the registration accuracy, it is essential to eliminate them by the Approximate Congruent Triangle Neighborhood (ACTN).Step 5: Point cloud registration. The RANSAC algorithm is applied for performing point cloud coarse registration, which can obtain the relationship between the template point cloud and the scene point cloud and provide an ideal initial position for fine registration. The fine registration works to obtain a final optimal transformation matrix using the Iterative Closest Point (ICP) algorithm.so are not eliminated.Due to the hardware design of the Percipio FM851-E2 3D vision sensor, external environmental interference and other factors, point cloud outliers are inevitable in the measurement. The pallet detection results will have errors if the outliers in the original scene point cloud QThe distance from arbitrary point In warehousing environments, the scene point cloud acquired by the Percipio FM851-E2 3D vision sensor contains a lot of redundant information, such as the grounds and the walls, which will decrease the calculation efficiency. Therefore, it is necessary to remove the useless planes in the scene point cloud . The spei. Randomly select three points from the scene point cloud QSE after removing outliers and obtain the parameters of the initial plane PI.Step 1: The plane equation in the three-dimensional point cloud is defined as:i from the point Pi to the initial plane PI and the angle I.Step 2: Calculate the distance Ds.Step 3: Repeat the above procedures until the number of the points in the plane reaches the threshold t, and remove the final fitted plane model to obtain the preprocessed scene point cloud Qs still contains a large number of points, which leads to low efficiency of feature extraction and matching. Selecting key points to simplify the point clouds can retain the features of the point clouds as much as possible while reducing the number of the points. The Intrinsic Shape Signatures (ISS) is a widely used algorithm with a fast calculation speed and high repeatability to realize key point extraction  and change interval Δr of the neighborhood search. Set the value of p, and s, which can usually be set to a fixed value.Step 1: Set the radius range [rConsidering the radius of interest is usually closer to j and eigenvalues Step 2: Calculate the covariance matrix Cneiborhood is established as a measure of unpredictability based on information entropy theory, and it is defined as [Step 3: The entropy function of local neighborhood Efined as :(8)EneigThe smaller the value of the information entropy, the smaller the uncertainty of the variable, which is the core of the Shannon entropy theory . AccordiHowever, the optimal neighborhood radius In order to avoid the limitation of the above assumptions for the scene point cloud and improve the estimation accuracy of the optimal neighborhood, a more general solution for calculating the optimal neighborhood radius i is the key points of the pallet point cloud. The specific calculation procedures of color feature and geometric feature are as follows:The ACFPFH feature descriptor consisting of a 3-dimensional HSV color feature and the 33-dimensional FPFH geometric feature is proposed in this section, which is shown as Equation (11):(1) Color feature calculationThe point cloud data acquired by the Percipio FM851-E2 3D vision sensor contain information such as the color and coordinates of the object. Due to the high correlation between components in RGB color space, color cognitive properties cannot be intuitively expressed. Therefore, RGB color space is not suitable for feature similarity detection. Compared with RGB color space, HSV color space is easier to distinguish and more consistent with human visual characteristics. H represents the hue, S represents the saturation and V represents the value. HSV color space is exploited to form a color feature descriptor of the key point or space .(12)V=m(2) Geometric feature calculationFPFH is an efficient local feature descriptor which reflects the normal relationship between query points and neighborhood points of point cloud data. The detailed calculation procedures are explained as follows:q in the middle of the figure is the query point, and the colored points Pq1-Pq5 in the black circle are the neighborhood points of Pq, and those blue points Pq6-Pq15 are the neighborhood points of the colored points Pq1–Pq5.Step 1: For each key point Step 2: The point pairs Taking the point Step 3: The angles qj, and s and Pt. Step 4: For each neighborhood point Accurate surface matching is an important prerequisite for point cloud registration, which directly affects the performance of pallet detection. For the traditional surface matching method, the one-way feature matching is performed and the method for eliminating incorrect matching point pairs only considers the relationship between points, leading to too many incorrect matching pairs. Therefore, a new surface matching method called BNNDR-ACTN is proposed, which includes feature matching based on the Bidirectional Nearest Neighbor Distance Ratio (BNNDR) and the incorrect matching point pairs’ elimination based on the Approximate Congruent Triangle Neighborhood (ACTN). The architecture of the proposed surface matching method is shown in Module 1: Feature matchingThe purpose of point cloud feature matching is to establish the relationship between the feature descriptors of the template point cloud and the scene point cloud, thereby obtaining the initial matching point pair.Step 1: Forward matchingDefine Step 2: Backward matchingFor each ACFPFH descriptor If the key points Module 2: Elimination of wrong matching point pairsThe surface of the object is rough and noisy, which leads to some mismatching point pairs. Therefore, after obtaining initial matching pairs, the next step is to eliminate the wrong matching point pairs.Step 3: Triangle neighborhood generationSelect a query point pair Step 4: Obtain the correct matching point pairsThe point pair Considering the stability of the triangle, each point in the point cloud is expanded into a triangular neighborhood. Therefore, the point-to-point matching problem is transformed into the neighborhood matching problem, which can obtain more feature information and improve registration accuracy. In addition, each point in the point cloud is regarded as the vertex of the triangle, which benefits the maintenance of the geometric characteristics of the original point cloud.The main task of point cloud coarse registration is to obtain the relationship between the template point cloud Step 1: Three correspondences are randomly selected to estimate the rigid transformation matrix 0 as the inlier point; otherwise, consider it as an exterior point.Step 2: Calculate the distance 0 is reached.Step 3: Repeat the above steps to obtain a different rigid transformation matrix and count its corresponding number of inliers point until the maximum iteration number IStep 4: Obtain the final rigid transformation matrix The Iterative Closest Point (ICP) algorithm is used to achieve point cloud fine registration. It is based on minimizing the error function to calculate the optimal rotation matrix and translation matrix. The specific procedures of fine registration are as follows:Step 1: For each point Step 2: Use the least square method to solve the rotation matrix Step 3: Repeat the above steps and obtain the optimal rotation matrix ® Core™ i7-11700kf CPU and 16 G memory.In order to validate the performance of the proposed ACFPFH feature descriptor and the overall registration method, two representative indicators are developed to evaluate the experiment results, and the details of these indicators are briefly described as follows. The experiment was performed using MATLAB Code on a desktop with 3.6 GHz inter(1) Precision-recall curveThe precision–recall curve (PRC) is used to evaluate the descriptiveness of a feature descriptor. The precision is calculated as the number of correct matching point pairs with respect to the total number of matching point pairs:The recall is calculated as the number of correct matching point pairs with respect to the number of key points of the template point cloud:(2) Root mean square errorRoot mean square error (RMSE) is the error evaluation index commonly used in point cloud registration, which represents the average of the sum of squared distances between the corresponding points of the two point clouds. It is defined as:In order to verify the effectiveness and feasibility of the proposed pallet detection method, a widely used industrial camera called the Percipio FM851-E2 3D vision sensor is adopted to acquire point cloud data for comparative analysis of the results. The vision sensor shown in The Percipio FM851-E2 3D vision sensor is mounted on the top of the carriage of a real automated guided vehicle, which means the camera will move along with the forks, as shown in For the pallets on the ground, the color image of the scene is acquired by the Percipio FM851-E2 3D vision sensor at the same distance of 500 mm, which is shown in It is necessary to determine the adaptive neighborhood radius for each point before calculating the ACFPFH feature descriptor. Given that the interval between two sampling points of point cloud data acquired by Percipio FM851-E2 3D vision sensor is 0.007 m, set the radius range Extract the HSV color components of the key points of the pallet template point cloud and the scene point cloud, and calculate the geometric feature based on the adaptive optimal neighborhood radius. The ACFPFH feature descriptor is obtained by superimposing the color and geometric features. The feature matching is completed with the distance ratio threshold of The PRC is used to evaluate the descriptiveness of a feature descriptor. The ACFPFH feature descriptor is compared with the classical feature descriptors including FPFH, CFPFH and Signature of Histogram of Orientation (SHOT) with the fixed neighborhood radius.The set Traditional feature descriptors such as SHOT and FPFH only describe the geometric feature of the pallet and ignore the color information, so the precision is lower. The CFPFH feature descriptor considers the HSV color information, which improves the precision. The neighborhood radiuses of the above three feature descriptors are obtained by complex and inefficient manual debugging methods, which are not suitable for all points in the point cloud. A large neighborhood radius leads to too many key points in the neighborhood, which reduces the speed of feature extraction. The ACFPFH feature descriptor not only contains color information but also adaptively selects the optimal neighborhood radius for each key point, so that it performs better in terms of effectiveness and precision.It is well known that the closer the curve is to the upper right, the better the performance of the feature descriptor in the PRC graph. It can be seen from The RMSE and runtime are used to evaluate the performance of the registration algorithms. Popular algorithms including ICP, SHOT + ICP, FPFH + ICP and CFPFH + ICP are selected to compare with the proposed method in this paper. The number of iterations, the RMSE and the runtime of the above methods are detailed in The following conclusions can be drawn from Shelves are widely used in intelligent manufacturing systems, which can improve the utilization rate of warehouse space and realize the rational allocation of resources while ensuring the quality of goods. Hence, it is necessary to complete the pallet detection of the shelf scene. The color image and the point cloud of the shelf scene are acquired with the same distance from the ground scene, as shown in Compared with the traditional ICP algorithm, the RMSE of the proposed method is greatly reduced, and the runtime is reduced from 29.523 s to 0.989 s, which proves that the efficiency and accuracy have been greatly improved. Compared with other modified ICP registration methods, the RMSE of the proposed method is the smallest, and the runtime is the shortest. In summary, the proposed method still achieves optimal performance in the shelf scene, which shows its effectiveness in different scenes. Furthermore, the above case studies demonstrate that the proposed method can be well applied in intelligent manufacturing systems to realize accurate and efficient pallet detection. In addition, feature descriptors can often determine the final performance in the process of point cloud registration. Combining a good feature descriptor with a good matching strategy would improve the efficiency of point cloud registration.A novel pallet detection method for automated guided vehicles based on point cloud data is proposed, which can be used for automated guided vehicles to perform automated and effective pallet handling, thereby promoting the transformation and upgrading of the manufacturing industry.A new Adaptive Color Fast Point Feature Histogram (ACFPFH) feature descriptor has been built for the description of pallet features, which overcomes shortcomings such as low efficiency, time-consumption, poor robustness, and random parameter selection in feature description.A new surface matching method called the Bidirectional Nearest Neighbor Distance Ratio-Approximate Congruent Triangle Neighborhood (BNNDR-ACTN) is proposed, which transforms the point-to-point matching problem into the neighborhood matching problem and can obtain more feature information and improve the detection accuracy.A novel pallet detection method for automated guided vehicles based on point cloud data is proposed in this paper. The contributions of this paper can be concluded as follows:Due to the measurement accuracy of the 3D vision sensor being easily affected by environmental factors such as illumination and obstacles, a more robust and efficient pallet detection method will be researched, which is suitable for more complex scenarios.


Millimeter-wave (MMW) radar is essential in roadside traffic perception scenarios and traffic safety control. For traffic risk assessment and early warning systems, MMW radar provides real-time position and velocity measurements as a crucial source of dynamic risk information. However, due to MMW radar’s measuring principle and hardware limitations, vehicle positioning errors are unavoidable, potentially causing misperception of the vehicle motion and interaction behavior. This paper analyzes the factors influencing the MMW radar positioning accuracy that are of major concern in the application of transportation systems. An analysis of the radar measuring principle and the distributions of the radar point cloud on the vehicle body under different scenarios are provided to determine the causes of the positioning error. Qualitative analyses of the radar positioning accuracy regarding radar installation height, radar sampling frequency, vehicle location, posture, and size are performed. The analyses are verified through simulated experiments. Based on the results, a general guideline for radar data processing in traffic risk assessment and early warning systems is proposed. With the rapid development of cooperative vehicle-infrastructure systems (CVISs), roadside perception systems play an essential role in modern transportation systems because they can provide a panoramic view of the road traffic and address inadequate vehicle detection . RoadsidFor roadside MMW radar-based traffic risk assessment and early warning systems , MMW radHowever, due to the measuring defects of MMW radars, challenges such as low angular resolution and poor elevation measurement have limited their measurement accuracy, leading to data quality issues such as vehicle positioning errors ,9. PositThe remainder of the article is organized as follows: The task of traffic risk assessment and early warning systems is first to identify the high-risk traffic environment or behavior and then send the warning message to the relevant parties. For microscopic level risk assessment, multiple indicators have been proposed to quantify the traffic risk. The longitudinal risk indicators include time to collision (TTC), inverse time to collision (TTC-I), time headway (THW), deceleration rate to avoid crash (DRAC), etc. ,14,15. LWhether it is for risk indicators or artificial field calculation, real-time risk assessment relies heavily on vehicle location and speed detections. The positioning error may cause misperception of the vehicle motion and interaction behavior, causing false alarms and warning failure. Most work on radar measurement accuracy analysis focuses on analyzing the signal processing procedure. The theoretical limits of frequency-modulated continuous wave (FMCW) ranging accuracy due to the tolerance of crystal oscillators were analyzed . In 20]20], the However, for applying roadside perception in transportation systems, MMW radar locates vehicles based on the bounding box inferred from the radar point cloud rather than a single radar radiation point. Lack of sufficient data points and side-only detections  can causMMW radar systems measure the object range and angle by extracting relevant information from echo signals. The signal processing module eliminates unwanted signals (such as clutter), processes or enhances the echo signal, then calculates the target range and angle. Typically, an FMCW ranging method  uses a sThe ranging error Regarding the angular measurement, a phase-based method uses the phase difference between echo signals received by multiple antennas. As shown in For the angular measurement, the measurement error can be derived asIt is shown that the measuring accuracy can be improved by using a more accurate phase meter , a bounding box is provided to estimate the area occupied by the target vehicle. Consequently, the anchor point for each bounding box is selected to form an association with historical trajectories. Finally, the position measurement is corrected by a filter algorithm to acquire the optimal position estimation. Probabilistic and hierarchical approaches are two of the main techniques used in data association . ConsideIn the roadside perception scenario for the MMW radars, a vehicle’s relative position and posture change continuously under different traffic conditions and driving behaviors. Moreover, due to insufficient measured points and side-only detections, the distribution of the radar point cloud for the vehicle also changes continuously with the vehicle’s shape and posture, causing positioning deviation at the target level. We propose to investigate this phenomenon from the perspective of the vehicle location, posture, and size so that insight can be provided for the higher-level application of radar data. The installation location of the radar module affects the aspect of the view from which the vehicle target is observed. A higher installation position of the radar module can reduce the probability of target occlusion . HoweverThe sampling frequency can affect the positioning accuracy through the tracking algorithm. The tracking algorithm gives position estimation of the target vehicle (correction) by combining the observation and the prediction. As shown in Radar detections of a target vehicle rely on the effective target scatterer, the part of the vehicle that can be illuminated by the radar beam. An MMW radar has a large monitoring range and is often installed at a certain pitch angle and far away from the target vehicle. Due to the influence of radar angle resolution and signal attenuation, as the vehicle passes through the FOV of the radar, the effective vehicle scatterer varies as shown in The lateral location of the vehicle can also affect the distribution of the radar point cloud on the vehicle body, as shown in As shown in As the relative posture of the vehicle in relation to the radar module changes under different driving scenarios and radar installation conditions, the shape of the effective scatterer and distribution of the point cloud on the vehicle body change accordingly, as shown in Different types of vehicles have different RCS features. The total RCS of a target can be calculated as the vector sum of each scatterer , as shown inTypically, we simplify the vehicle model as a rectangle with the parameters length, width, and height, as shown in As the ground truth of a target vehicle location can be difficult to obtain, we conducted simulated experiments to analyze the radar positioning accuracy based on radar installation height, vehicle location, posture, and size. Leveraging MATLAB’s Automated Driving Toolbox and Radar Toolbox, we created a roadside perception scenario and generated synthetic radar detections using a statistical radar model. The basic parameters of the simulated MMW radar are shown in We set up a six-lane road section with a 5 m high gantry and installed the radar module at the center of the gantry, facing the x-direction with a 5° pitch angle downward. The radar FOV is shown in To evaluate the influence of radar installation height, we set up the following radar installation scenario: (1) height: The simulation results are shown in To evaluate the influence of radar sampling frequency, the target vehicle was set to run from  to  at a constant velocity of 20 m/s. A simple object tracking algorithm based on Kalman filter was used to acquire the vehicle trajectory. We observed the y-direction positioning error to evaluate the influence of the sampling frequency on the tracking algorithm and compared it against the x-direction positioning error characteristic. The sampling frequency was set to 25 Hz, 50 Hz, and 100 Hz as factors of the base simulation frequency. As shown in To analyze the influence of vehicle position, we divided the road into several sections. As shown in To evaluate the influence of the vehicle posture, we fixed the location of the vehicle to To evaluate the influence of the vehicle size, we compared the error characteristics of vehicles with different lengths, widths, and heights. We fixed the vehicle location to The influence of vehicle length was evaluated at 3.0, 4.7, and 6.0 m. As shown in The influence of vehicle width was evaluated at 1.3, 1.8, and 2.3 m. As shown in The influence of vehicle height was evaluated at 0.5, 1.4, and 3.0 m. As shown in Based on the experiment results, we propose the following guidelines for MMW radar data processing in risk assessment and early warning systems. As shown in the previous results, the positioning error is greater when the vehicle has not fully entered the FOV or is driving away from the FOV. Therefore, the original FOV can be divided into several zones, as shown in Similarly, greater positioning error is found when the vehicle yaw angle is near As shown in In this paper, we analyze the influence of radar installation height, vehicle location, posture, and size on the positioning accuracy of MMW radar. In the numerical simulation, we set up a roadside perception scenario where the MMW radar is mounted at the center of a gantry, overlooking the road traffic. The following conclusions are drawn from the experiment results.Influence of radar installation height: When the radar is installed at a higher position with a greater pitch angle to monitor the same section of road, a larger longitudinal positioning error is observed when the vehicle is driving away from the radar FOV. Influence of radar sampling frequency: Greater tracking error on the y-direction is observed when the sampling frequency is lower. The tracking error on the x-direction is not significantly influenced by the sampling frequency.Influence of vehicle location: When the vehicle passes through the radar FOV, the radar positioning in the longitudinal direction is first positively and then negatively biased. In the lateral positioning, the radar positioning biases to the left when the vehicle locates on the right side of the radar central beam, and vice versa.Influence of vehicle posture: A large positioning deviation is observed when the vehicle yaw angle is at Influence of vehicle size: When the vehicle is closer to the radar module, the vehicle height can severely affect longitudinal positioning. The vehicle length causes longitudinal positioning errors when the vehicle is further from the radar module. A greater lateral positioning error is observed when the vehicle is wider.Based on the above conclusions, a general guideline for MMW radar data processing in risk assessment and early warning systems is proposed to acquire more accurate risk information. In the future, real-world experiments can be conducted to verify the simulation results and the effectiveness of the proposed guidelines. Plenty of work still needs to be carried out to tackle the high environment noises, target occlusions, and efficient sensor calibration.


This finding implies that the choice of performance measure is not very critical when one is interested solely in the overall performance. In the third and last step, mixed-effects models were derived for the performance measures of interest to establish the importance of different turn-related actions for each measure, again for both the entire data set and the four subgroups separately. The results of this analysis revealed that performance measures with short(er) distances are more sensitive to changes in the adaptation time and reflect the wall contact time better than performance measures with long(er) distances, which in contrast are more useful if the focus is on the approach speed prior to the turn. In this final analysis, various effects of performance level and sex were found on the technical execution of the tumble turn.Although the tumble turn in swimming has been studied extensively, no consensus exists about which measure is best suited to capture its performance. The aim of this study was to better understand the implications of choosing a particular distance-based performance measure for assessing and investigating tumble turn performance in freestyle swimming. To this end, a large set of retrospective turn data consisting of 2,813 turns performed by 160 swimmers was analyzed statistically in three steps. First, a mixed-effects model was derived for the entire data set, which showed that both performance level and sex had clear effects on the distance-based performance measures and performance determining variables studied in the literature. Second, repeated measures correlations were calculated for the entire data set and four performance level- and sex-based subgroups to determine the level of association between the performance measures. This analysis revealed that the performance measures were strongly correlated ( Turning is an important component of competitive swimming, which determines a substantial portion of the overall swim performance. Research has shown that the accumulated duration of turns amounts to at least 19% of the total race time in long-course races and up to 44% in short-course races  crossing predefined fixed distances toward and away from the wall. The limitation of the first definition is that a lower score on this performance measure (a shorter time) does not necessarily indicate a superior turn performance compared to a higher score (a longer time). This is due to the velocity profile of the swimmer after the push-off from the wall and the subsequent underwater phase. The velocity after the push-off from the wall is higher than the free-swimming speed and decreases over time due to drag until the speed is equal to the free-swimming speed, at which the swimmer ideally transfers to free swimming  available performance measures. This can be accomplished by deriving linear mixed-effects models to determine which specific turn-related actions account for (most of) the variance of each performance measure.Based on logical grounds, we expected the technical variables associated with wall contact, notably the adaptation time and wall contact time, to account for more of the variance of the performance measures with a short(er) distance covered  than for performance measures with a long(er) distance covered . Conversely, we expected technical variables associated with the underwater phase, notably the push-off angle, the deepest point of the hip reached underwater and the break-out distance, to account for more of the variance of performance measures with a long(er) distance than performance measures with a short(er) distance covered.For this study, extensive retrospective freestyle turn data from 160 swimmers  were analyzed. The participants, or their legal guardians in case they were 16 years of age or younger, signed an informed consent form stating that their turn data would be used anonymously for the purpose of scientific research. FINA points were determined to assess the performance level of the swimmers. To this end, the race times on any freestyle event (long course or short course) swum during the period from 2010 to 2021 in which the turn data were collected, were converted to FINA points based on the FINA point score of 2021 .The fixed video system in the training pool of De Tongelreep consists of four cameras  that are embedded in the pool's lateral sidewall . The video data from the cameras were acquired using the software package Streampix 7 . The cameras were synchronized by means of an external trigger pulse generator (NI-DAQmx Pulse Generator). During the trials, the swimmers began swimming about 15 m from the wall (before the turn), sprinted toward the wall, turned and sprinted back until they were positioned well beyond the 15-m mark with their hip. All trials were recorded during regular training sessions and performed at maximal or race pace effort to mimic competitive races. Whether or not this was actually accomplished was not considered a major concern since having considerable variation in tumble turn performances is beneficial for the to-be-performed statistical analyses.All video recordings were analyzed manually using a custom-made software package called Turnanalyzer . The intrinsic parameters of the cameras were determined with the Camera Calibration Toolbox in Matlab  the performance measures, the data set would be split into two subgroups . Similarly, if main effects of both performance level and sex would be found for (the majority of) the performance measures, the data would be split into four subgroups  between two independent variables, the one with the highest absolute correlation coefficient with the performance measure was preserved as a predictor in the model while the other one was removed.As a first pass on the data, boxplots and histograms were made and inspected for all independent variables to identify outliers amongst the independent predictors. Following this, collinearity was assessed by calculating the repeated measures correlation coefficients between all independent variables. In case there was a strong repeated measures correlation  and significantly (p < 0.001) correlated. For the sake of clarity, the repeated measures correlations were presented for the entire data set only, since the results for the four subgroups, which are presented in As can be appreciated from the repeated measures correlation matrices presented in r = 0.73), indicating collinearity between these variables. Additionally, the absolute repeated measures correlation coefficient with the performance measures was lower . For all six performance measures, a prediction model was derived with athlete ID as a random effect and the seven remaining technical variables as independent predictors.The boxplots and histograms of the technical variables revealed no outliers. According to the procedure described in the Materials and methods section, the horizontal initiation distance was excluded from the model because it was highly correlated with the adaptation time  distances. The adaptation time was a stronger performance predictor in the female swimmers than the male swimmers .In the sub-elite female swimmers, all three main predictors  contributed to a similar degree, unlike the other three subgroups in which one main predictor clearly contributed more than the other two  .The present study examined the implications of selecting a particular distance-based performance measure for the tumble turn with regard to the overall turn performance and the underlying performance determining variables. To this end, three statistical analyses were performed: (1) a foundational analysis to examine the impact of performance level and sex on the selected performance measures and the performance determining variables, (2) a coarse-grained analysis of the correlation strengths between the performance measures of interest, and (3) a fine-grained analysis of the technical variables that account for the variation in each of those performance measures.As expected, the foundational analysis revealed significant main effects of both performance level and sex on the performance measures and technical variables of interest. Based on these results the data set was divided into four subgroups: elite male, elite female, sub-elite male and sub-elite female. All subsequent analyses were done on the entire data set and on the four subgroups separately to gain insight into any subgroup-specific differences in performance and the adopted turn technique.The level of association between the fixed-distance performance measures of interest was assessed by calculating repeated measures correlation coefficients for all possible pairs of performance measures, both for the entire set of turn data and the four subgroups. This analysis revealed a high overall level of association with correlation coefficients ranging from 0.84 to 0.99. Although this result was expected on logical grounds since the performance measures covering longer distances overlap with the performance measures covering smaller distances , it has, to our knowledge, not been established or highlighted before in the literature. The strong correlation between the various performance measures implies that if one is interested solely in assessing the overall turn performance, the choice of performance measures is less critical. This finding was exacerbated by the marginal unsystematic differences in the results obtained between the four subgroups, which showed similar correlation patterns independent of performance level and sex. The small differences in the level of association between the various performance measures may have been due to differences in the turn-related actions and amount of free-swimming covered by the different performance measures .However, even though all correlation coefficients were higher than 0.8, it appeared that the included distance after the turn had a stronger effect than the distance before the turn since the correlation coefficient decreased pair-wise with different out-off-the-wall distances, but not with different to-the-wall distances see . This fiThe fine-grained sensitivity analysis revealed which turn-related actions are reflected predominantly in the performance measures of interest. This was achieved by finding the best fitting linear mixed-effects model for each performance measure using seven (eight minus one) technical variables as independent predictors and athlete ID as a random effect.R2 value ranging from 70 to 91% explained variance. This is in contrast to the findings of Nicol et al., who did not find any of the technical variables to be a significant predictor of the overall turn time . This could have resulted in an overall higher variability of the included variables and an underestimation of their contribution.The contribution of the adaptation time and WCT decreased compared to the speed-in as the distance covered by the performance measures increased for all subgroups, except for the elite female swimmers. This decrease could be expected since these variables play a role early in the turn; therefore, even if their impact on turn time is large, it will be diluted by the impact of the actions later in the turn. This effect will be especially strong for the adaptation time. Also, the ratio of the WCT and the turn time decreases the longer the distance covered, which automatically reduces the impact. This result confirms our expectation that if the WCT is the focus of interest, a performance measure with short(er) distances should be chosen. The contradictory finding for the elite female swimmers could be explained by looking further into the characteristics of this subgroup, which contained a relatively small number of swimmers  between the subgroups, not one predictor appeared more dominant than the other two predictors in the sub-elite female swimmers. In contrast, WCT stood out as the main contributor in the elite male and female swimmers, while speed-in was the main contributor in the sub-elite male swimmers. This might suggest that the variation of turn strategies was markedly higher in the sub-elite female swimmers than in the other subgroups.The remaining three technical variables, the push-off angle, the distance of the first kick and the deepest point in the underwater trajectory, were only minor contributors to some of the models. From the literature, a negative estimate was expected for the distance of the first kick  of the Faculty of Behavioural and Movement Sciences of the Vrije Universiteit Amststerdam. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.PK, WA, SS, and PB conceptualized and designed the study. PK, WA, and SD performed the literature search and data processing. SS, PK, and WA were involved in data collection. PK, SD, SS, and PB drafted the current manuscript. All authors contributed substantially to the interpretation of the data, revised it critically and approved the final version of the manuscript before submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.