One major problem related to sandy soil is its low shear strength and cohesion in engineering. Although much effort has been made to strengthen sand mass with satisfactory performances, most undertakings lack environmental considerations. Thus, a combination of natural fiber and macromolecule polymer material attempts to achieve both strength and eco-friendliness. In the present investigation, sisal fiber (SF) and water-based polyurethane (PU) were used to reinforce sand. A series of unconfined compression tests were carried out on sand specimens at different percentages of fiber contents  and polymer contents . The results showed within our test range that the unconfined compressive strength (UCS) as well as post-peak strength of specimens increase with fiber and polymer contents. The inclusion of fiber and polymer significantly improve the ductility of specimens. The effect of dry densities on UCS were studied with three proportions. It is found that a high dry density led to an increase of UCS due to an effective contact area increase. The interactions were studied by observation through scanning electron microscopy (SEM) images. The presence of water-based polyurethane has the potential to improve the interparticle cohesion of sand due to its unique network membrane structure. The fiber reinforcement benefit depends strongly on the friction, interlocking force, and bond strength at the interface. Soil can often be regarded as a combination of four basic types: gravel, sand, clay, and silt . It geneIt is known that the clay content of sandy soil is relatively low, while sand content is higher than 50%, resulting in low interparticle cohesion. Generally, the structural binding and aggregation between soil particles are mainly conditioned by the higher amounts of clay particles and organic content in soils . Thus, iOn the other hand, it has a long-applied history for including randomly distributed, discrete fibers into the matrix  to provide an improvement in the mechanical behavior of the soil composite. Many research studies have received encouraging results that prove the effective usage of fibers for soil reinforcement ,17,18,19Though a good performance of soils with fiber and chemical additives  has been observed in many research studies, it is expected that the combination of fiber and polymer can achieve better enhancement effect on the strength of sand through their synergetic effect. In this attempt, a sisal fiber–polyurethane polymer mixture is a further step in this direction.In this paper, a series of laboratory studies were performed to further understand the effects of randomly distributed sisal fiber (SF) and polyurethane (PU) for improving the unconfined compressive strength of sand. The unconfined compression tests were carried out on sand specimens with different amounts of fibers and polymers. The scanning electron microscopy (SEM) tests were also conducted to reveal the internal structure of reinforced sand mass and primarily investigate its strengthening mechanism.The sand used in the present experiment was taken from the area of Nanjing, China. It was oven-dried, crushed, and passed through a two-mm sieve in the laboratory for spare. The physical properties of sand are listed in The polymer used in the experimental tests was water-based polyurethane. Starting from polyethylene glycol , polypropylene glycol , and toluene diisocyanate , the prepolymer was synthesized via condensation polymerization. Due to the hydrophilic segments or groups in the molecular chain, the prepolymer can better respond to water. Thus, stable polyurea was prepared via emulsification in water and chain growth. The physical properties of the polymer are summarized in The photograph of the short SF  is shown in 3 and an initial water content of 10% for easy demolding. With respect to the effect of dry density, four different values adopted for 3, 1.5 g/cm3, 1.55 g/cm3, and 1.6 g/cm3. To control the number of test specimens, three specific proportions of SF and PU were adopted with For evaluating the effect of different SF and PU content on strength behavior, the orthogonal test was carried out with four SF contents  standards (D2166-00), and shaped into a cylinder  were selected for comparison, as given in To ensure the repeatability of the tests, the stress–strain curves of the three parallel specimens prepared at the same conditions  with different fiber contents obtained from unconfined compression tests are presented in The typical stress–strain curves of specimens ; thus, it is important to meet the requirements of improving the mechanical properties of sand by enhancing the cohesion to a certain measure . UndoubtWith respect to the effect of fiber content on failure characteristics, the failure process and mode of specimens subjected to axial stress are revealed in In general, the shear cracks randomly intersect the fiber, leading to extremely variable anchoring lengths. Along the shear plane, the debonding and pull-out failure of fibers are prone to take place on one end, which has a shorter effective anchorage length on the side of the failure plane. Due to the low skin frictional resistance of the fiber having the shortest embedded lengths, the fiber slippage in composites becomes increasingly susceptible to being caused by the mobilized tensile stress developed in the fiber. Such pull-out failure makes fibers unable to give full play to the reinforcement effect. As a matter of fact, the real situation of randomly distributed fibers in soil is more complicated than the employed assumption due to the variation of the intersection angle between the fiber and the failure plane. More extensive studies should be conducted to refine the results.Accordingly, it seems that fiber length is also a non-negligible parameter affecting the compressional behavior of the specimen ,30. PrevThe corresponding UCS is presented in 3) treated with different PU contents at the same fiber contents. It can be observed that an increase in the PU content leads to a great enhancement in peak axial stress. However, as mentioned above, at a certain point , the strength reaches a plateau and the subsequent gains become much harder to get because of complete pore filling, which restricts the improvement efficiency of PU. It should be noted that the PU content has a great influence on the overall shape of the stress–strain curves. By comparison, strain hardening is more obvious with the increase of the PU content, suggesting a high toughness and strain resistance for treated sand. Obviously, PU treatment has a stronger effect on reforming the internal structure of the sand body compared with fiber reinforcement. This is because the cohesion is affected slightly by addition of fiber, being basically a function of cementation induced by polymers [polymers . GreaterDue to the presence of polymers, the sand particles are finely structured; thus, they exhibit a better strength behavior and show sufficient ductility. As shown in The reduction in brittleness can be attributed to the interparticle desiccation induced by the elastic polymeric materials . After sThe UCS increases with dry density can be attributed to the contact conditions. The required compaction is determined by dry density, which means that greater compaction is needed for the specimens with the same dimension to achieve higher dry density. This leads to a lower void ratio and a smaller pore diameter; thus, it gives rise to the effective contact area between the sand particles. The efficiency of both fibers and polymers is achieved by the contact conditions of sand particles. A previous study showed that the effective contact area directly influences the magnitude of the interfacial friction and adhesion . For fibGenerally, the organic matter in soil affects the soil structure . Figure The presence of polymers has ability to enhance sand strength with the provision of sufficient cohesive force, a dense arrangement of sand particles, and a good impact absorption capacity due to its unique structure. Therefore, the interaction between sand particles and polymers is mainly conditioned by the following factors: (1) particle micromorphology  and grain size distribution, which directly or indirectly affect the adhesion effect  of polymers on sand particles; (2) ambient temperature, which may accelerate the fragmentation and detachment of membranes from sand particles under high temperature  theSEM images of fiber–polymer-reinforced sand are presented in Within our test range, contrary to the variation of UCS with PU content, the UCS of specimens increases monotonously with fiber content, showing no trend of saturation nor a decreasing trend. However, too many fibers may lead to an adverse effect on the strengthening because the fibers adhere to each other to form lumps, which lead to insufficient contact with particles ,42. Furt(1)The unconfined compressive strength and residual strength of the sand specimen increase with an increase in the fiber and polymer content within our test range. Owing to the inclusion of sisal fiber and polymer, sand specimens exhibit a better strength behavior and show sufficient ductility.(2)Water-based polyurethane (PU) treatment is the precondition for fiber reinforcement. It provides a suitable structural environment for fibers by increasing the interparticle cohesion of sand.(3)An increase in the sand dry density has an affirmative effect on improving the unconfined compressive strength of the specimens. This is mainly attributed to the interfacial interactions between the fiber and the matrix, which is further enhanced due to the more effective contact area at a high, dry density.(4)The presence of water-based polyurethane (PU) has the potential to improve the interparticle cohesion of sand due to its unique network membrane structure. These elastic polymeric membranes effectively improve the interparticle stress and restrict particle motion through surface enwrapping, pore filling, and a connecting effect.(5)Fiber reinforcement is conditioned by the interfacial mechanical interactions between the fiber and the matrix. Friction, the interlocking force, and bond strength at the interface are the dominant factors controlling the fiber reinforcement benefit.A series of unconfined compression tests were conducted to study the unconfined compressive strength and mechanical behavior of short sisal fiber-reinforced and water-based polyurethane-treated sand. The interfacial mechanical interaction between sand particles, fibers, and polymers was investigated. The critical factors affecting polymer treatment and fiber reinforcement were discussed. The main conclusions from the present work can be summarized as follows:Although the interfacial interactions between fibers and polymer–sand composites play an important role in reinforcing soil systems, few research efforts have yet been carried out. A proper understanding of the interaction mechanism along the fiber–soil interfaces can be obtained through quantitively analyzing the interfacial mechanical parameters, such as the interfacial bonding and shear strength. Practical applications are also of great concern, since the laboratory experiments showed this treatment had the potential to improve the mechanical properties of sand by strengthening its internal structure. However, there are many different stimuli in the practical applications, such as temperature, ultraviolet rays, water, chemical mediators, etc. Further research studies are needed to determine whether the polymers are able to adapt to the complex environmental conditions.


This study presents a new and improved approach to the rapid and green fabrication of highly conductive microscale silver structures on low-cost transparent polyethylene terephthalate (PET) flexible substrate. In this new laser direct synthesis and pattering (LDSP) process, silver microstructures are simultaneously synthesized and laid down in a predetermined pattern using a low power continuous wave (CW) laser. The silver ion processing solution, which is transparent and reactive, contains a red azo dye as the absorbing material. The silver pattern is formed by photothermochemical reduction of the silver ions induced by the focused CW laser beam. In this improved LDSP process, the non-toxic additive in the transparent ionic solution absorbs energy from a low cost CW visible laser without the need for the introduction of any hazardous chemical process. Tests were carried out to determine the durability of the conductive patterns, and numerical analyses of the thermal and fluid transport were performed to investigate the morphology of the deposited patterns. This technology is an advanced method for preparing micro-scale circuitry on an inexpensive, flexible, and transparent polymer substrate that is fast, environmentally benign, and shows potential for Roll-to-Roll manufacture. The market and applications of flexible, stretchable, and wearable electronics have increased remarkably in recent years. The search for rapid and cost-effective ways to combine electronics with flexible and stretchable substrates has also grown enormously. One central issue that challenges the fabrication of flexible electronics is that most flexible substrates, such as polymers, are unsuitable for the conventional photolithography used in microelectronic fabrication because they have poor chemical and thermal resistance. The thermal issue also leads to poor adhesion between the metallic materials and the polymer substrate caused by significantly large differences in thermal and mechanical properties . To overHowever, all these nanomaterial-based processes require the synthesis of metal nanomaterials, which is usually a complex, time-consuming, and costly procedure. The coating and recycling of nanomaterial on a polymer substrate is also very challenging. The novel laser direct synthesis and pattering (LDSP) technology presented here provides a low temperature, green, nanoparticle-free process that does not require masks, or vacuum, and is ideal for the rapid fabrication of flexible electronics ,11. In cAlthough conceptually promising, the feasibility and the consequences of this approach remain to be proved. In addition, for realization of economic flexible electronics, low-cost transparent substrates such as polyethylene terephthalate (PET) film should be used. However, this kind of substrate is usually vulnerable to elevated temperature during the fabrication process. The thermal impact on the PET substrate subjected to the modified LDSP process also needs to be investigated. In this study, an improved LDSP process has been modified as described. Specifically, a red dye was added to the transparent nanoparticle-free reactive ion solution. The solution was applied to the transparent PET substrate, after which the LDSP process, where a focused laser beam was directed through the underside of the transparent PET substrate, was carried out. The red-colored reactive solution serves as the absorber of photon energy and converts it to thermal energy for the chemical reaction. The effects of the additive materials, concentration and the number of laser scans were all investigated. Electrical conductivities and the mechanical durability of the resulting nano-/microscale conductive patterns on the transparent PET substrate were measured and discussed. It is emphasized that, and shown in the results, using the red dye to make the reactive solution absorptive is a simple and cost-effective way to expand the application of the LDSP technology to include transparent polymer substrates. Unlike the other possible types of additives where nano-powders are used to form adsorptive colloids, the reactive solution mixed with red dye used in this study is free from precipitation and unwanted chemical reactions caused by the use of additives. Numerical simulation was also carried out on the heat and flow transport near the laser focal spot. Discussions of the impact on the resulting silver line structures from the temperature and velocity fields are also provided.The transparent reactive silver ion solution was prepared following a reported procedure  with mod® Beam Profiler SP620U, Ophir Optronics®, Jerusalem, Israel). The focused laser beam was then directed by a programmable galvanometer scanner system  to selectively scan a predetermined light pattern through the transparent PET substrate, the energy of which was absorbed by the process solution. Since the PET substrate is transparent and thin, there was no significant extinction of the laser light and the process solution absorbed the laser energy very effectively. 2 μm) to the PET surface. A small and well controlled thermal impact area is essential for the LDSP process to ensure good resolution as well as the recovery of unreacted process solution. After the LDSP process was completed, the PET surface was washed carefully with deionized water and ethanol and air-dried to remove any remaining process solution.The diagram in In this study, the laser power was fixed at 100 mW, and the laser scan speed was 10 mm/s. The number of laser scans and the red-dye concentrations were varied to conduct the parametric studies. −7 Ω·m, which is approximately 10 times that of pure silver. This can again be attributed to the fact that the composition of the conductive line contains a fraction of carbon and oxygen. Nevertheless, the conductivity of the line is sufficient to conduct electricity effectively as demonstrated in ® software  was applied in the current study. Details of the numerical model can be found in an earlier report [Qabs from the absorption of a Gaussian laser beam moving in the liquid can be determined by [R is the reflectivity, γ is the absorption coefficient of the process solution estimated from Ipk is the peak laser intensity, and ω is determined from the size of the laser beam on the x-y plane of the PET substrate. In the current simulation, the surface reflectivity from the PET substrate and the interfaces of the PET and process solution were assumed to be negligible. Chemical reactions and liquid-vapor phase change were also neglected in this analysis considering that there was no significant boiling observed during the process.Numerical simulations were carried out to further investigate the transport phenomena during the fabrication process. COMSOL Multiphysicsr report  and in [r report ,20. In cmined by ,20,21: flexible substrate was successfully demonstrated. Predetermined silver pattern was formed by photothermochemical reduction of the silver ions induced by the focused CW laser beam in a processing solution containing a red azo dye as the absorbing material. The effects of the additive materials, concentration, and the number of laser scans were all investigated. The electrical resistivity of the silver line was estimated to be 2.25 × 10


The nursing shortage is of worldwide concern, with nursing student retention acknowledged as a priority. As a fundamental step towards exploring factors that can guide the implementation of strategic approaches to retain undergraduate nursing students and prevent their attrition, the aim of this study is to examine the motivation for choosing nursing studies of first-year nursing students within the theoretical framework of self-determination theory.We conducted a study at the Medical School of the University of Palermo. A total of 133 first-year nursing students completed a two-part questionnaire: a measure of socio-demographic aspects and an open question about their motivation for choosing nursing studies. Students’ responses were analysed using thematic analysis. Dimensional analysis was performed in order to verify an organization along one dimension, in agreement with the differentiation of the autonomous and controlled types of motivation of self-determination theory. A person-centred approach was utilised to define motivational profiles able to characterize clusters of students according to both quality and quantity of motivation.A set of 18 categories was developed. The factor analysis has shown that nursing students’ motivations can be organized along one dimension, in alignment with the differentiation of the autonomous and controlled forms of motivation of self-determination theory. Through adoption of a person-centred approach, four motivational profiles were identified: a) students with good quality motivation profile (high autonomous and low controlled); b) students with poor quality motivation profile (low autonomous and high controlled); c) students with low quantity motivation profile (low autonomous and low controlled); d) students with low quantity and poor quality motivation profile .Importance of this research includes the possibility to interpret nursing students’ reasons within the theoretical framework of self-determination theory, a well-grounded model able to offer useful information to academic nursing schools, in order to promote effective strategies to foster and support student motivation. Nursing is a profession of vital importance. Nurses play a significant role in the promotion of health, prevention of disease and care of the sick. The shortage of nurses in health systems is of worldwide concern , 2 and aThe nursing student attrition rate is one of the factors that negatively affects the satisfaction of health professional workforce demand. Planning approaches to promote the retention of undergraduate nursing students, especially during the first year, is a strategy to face the challenges of the shortfall of qualified nurses. The efficacy of these strategies requires better comprehension of the factors associated with nursing student attrition through fostering and supporting specific affective factors positively related to retention, and reducing modifiable factors positively related to attrition. Different levels and types of reasons and factors contributing to nursing students’ attrition have been identified –8. PersoIn the present study, the focus is on undergraduate student motivation in choosing nursing studies and the nursing profession. Comprehending the career motives for this choice is important in order to design specific strategies to promote undergraduate nursing student retention –15.Motivation is a complex construct with multiple components. It is the result of a set of processes of activation and orientation of behaviour and action, aimed at the realization of a specific purpose. In the multifaceted process of choosing an academic degree course and a career, different sources, goals and reasons which drive intentional actions are present as co-occurring factors , 16. StuThese motivations, due to their frequent occurrence, are often generic and their use can make it difficult to clearly understand the actual motivational process behind the student’s declaration. So, a similar term may refer to an internal process , or an external one . Any strategy that want to increase nursing students’ retention and set up efficient learning approaches, has to be based on a deeper understanding of their motivations. The use of structured questionnaires can help to avoid some inconsistencies. These tools are not very simple to implement, and the interpretation of the results requires a deep knowledge of the theory supporting their creation. Generally, the constructs are rigorously defined and the questions are strictly focused to measure only these factors. On the other hand, the use of open questions simplifies the gathering of self-reported motivations, and allows students to describe their beliefs and emotions about their choice of the degree course. However, it transfers to the researcher the demanding task of interpreting the students’ answers, and does not allow them to extend the results beyond the specific study.This study illustrates how the theoretical framework of self-determination theory (SDT) , 25 can SDT is a theory of human motivation that hypothesizes that people pursue goal content through different regulatory processes. It identify three different categories of motivation that extend along a continuum of increasing self-determination: amotivation, controlled motivation and autonomous motivation . Along tThe self-determination continuum, showing the motivational, self-regulatory, and perceived locus of causality bases of behaviours that vary in the degree to which they are self-determined, is illustrated in Fig. Amotivation is the state of lacking an intention to act [n to act , 25. Amon to act .Extrinsic motivation can vary greatly in the degree to which it is autonomous and it refers to pursuing a goal for reasons that are external to the activity itself. Considering the amount of relative autonomy, four types of regulation are recognized, namely external regulation, introjected regulation, identified regulation and integrated regulation [Externally regulated motivation is the classic case of extrinsic motivation. It is low in self-determination, and the origin of individual’ behaviours is a particular external contingency, such as the achievement of an external reward that is considered to be positive, or the avoidance of a consequence perceived as negative. The control of goal-directed activity is entirely external. Introjected regulation refers to people that pursue goal directed behaviours for specific contingencies and consequences administered by individuals to themselves. Introjected regulation has been in part internalized, but it is a relatively instable form of regulation [controlled motivation.gulation , 25. Thegulation , 26. ExtIdentified regulation is a more autonomous form of extrinsic motivation. Identification is a process through which people comprehend the value of an activity, and regulation is more internalized. The control is internal and the activity is sustained by self-regulation, and the objective has a value for the person.Integrated regulation is the most autonomous form of extrinsic motivation. Integration is the process through which people perform an activity, integrating the identification with some aspect of the self. The control and the regulation are internal. Individuals feel competent when they perform an activity, and consider the activity as meaningful. An integrated regulation motivational state shares more characteristics with intrinsic motivation. Nevertheless, the activity is carried out to obtain a goal or outcomes that are different from the activity itself.intrinsic motivation is a prototype of self-determined choice and activity. When intrinsically motivated, individual pursue a goal because of the inherent feeling of pleasure, enjoyment and satisfaction in the activity. The purpose corresponds with the activity itself. SDT suggests that people are intrinsically motivated to do something when they have the opportunity to autonomously choose meaningful activities that support their self-expression and that allow them to use their competence.Finally, well-internalized motivations; well-internalized and intrinsic motivations are the two subcomponents of autonomous motivation.Identified and integrated regulation can be classified as autonomous motivation profiles are associated to more successful learning outcomes and success, over time, than controlled motivation [autonomous motivation is associated to the use of deep cognitive strategy and reflection in learning [Many research studies have investigated the relationship between motivation and learning. Student motivation influences learning, study behaviour and achievement, and it is related to academic success . Researctivation , 29. Morlearning , 31. Intlearning . Studentlearning , and thelearning . Besidescontrolled vs. autonomous, is a preliminary objective for any interventions aimed at reducing attrition and improving academic success.Consequently, to understand the quality of personal motivation, in terms of The aim of the present study was to identify, using a qualitative research approach, representative categories able to summarise the content of first-year students’ self-reported motivations for choosing nursing studies. The categories were analysed within the theoretical framework of the self-determination theory, in order to verify their organization along a continuum scale, ranging from controlled to autonomous motivation.Although previous qualitative studies that investigate self-reported motivation for choosing nursing studies are different, they have not usually examined students’ motivational statements from an SDT perspective. In a research study the statements on motivations of applicants for medical studies were analysed within an SDT framework . This stIn this study, a methodology is proposed to overcome some of the biases typical of self-report data. The qualitative analysis of the nursing students’ answers was integrated with multi-dimensional scaling techniques aimed at interpreting the results of the qualitative analysis in the theory-grounded framework of SDT.The student nurses’ answers have been elaborated using qualitative and quantitative methods. The qualitative analysis was aimed at interpreting an abundant variety of personal information about career and study choices, instead of using defined tests in career choices, in which students have to select from limited answer options. The outcome of this analysis was a group of categories summarizing the main identified reasons.The quantitative analysis used the SDT framework to organize the categories along the dimension controlled/autonomous. This allowed a comparison between the results of this study and other studies that used scales to measure student nurse motivation, giving external confirmation of the validity of the proposed interpretation.The study was conducted at the School of Medicine of the University of Palermo. According to the local ethical policy, no prior formal authorization approval by the Ethics Committee was necessary. We communicated the qualitative study design to the Institutional board of the Undergraduate Nursing Course guaranteeing that ethical standards would be met and we received approval.Ntot=140) enrolled in the first year of the nursing degree were invited to participate in the study. One researcher (LM), a general psychology teacher in the nursing degree course, invited students attending her lectures to participate in the study.Data collection was carried out on entry to the nursing degree course, at the beginning of the first month of the first semester. All undergraduate students  the aim of the research was illustrated; (b) participation was voluntary, and students could decide to interrupt their participation at any moment; (c) the students were reassured about the confidentiality of the data collected, which was only managed by researchers; (d) each name was replaced with a unique code number.During the same session, LM presented the procedure for taking part in the study. Data was collected by filling in an on-line questionnaire by means of a web-platform structured for the purposes of the present research. Data was retrieved only after obtaining voluntary informed consent, in the presence of LM. The duration of the entire session was about 30 minutes.The questionnaire consisted in a two-part form: a) demographic survey aimed at gathering some demographic data about the students; b) motivation for choosing nursing studies.Demographic survey. The demographic questions were concerned with basic demographic information such as age and sex. Students were also asked if they had previously attended other university courses and if so, specify the name of the degree course.Motivation for choosing nursing studies. An open question was asked to gather information about student motivation regarding their decision to enrol on the university nursing course. Students had to answer the question: “Describe your reasons for choosing the nursing degree course”.N=133. Seven students chose to not participate in the research. Participants included a total of 84 females (63%) and 49 males (37%), aged from 18 to 41 . Demographic data of student participants, regarding gender and age distribution, are reported in Table The number of first-year nursing students who participated in the study and answered all questions was Thirty-six percent of the students had previously attended other university courses. Previous university student enrolments mainly consisted of attending healthcare sector courses, like pharmacy and psychiatric rehabilitation techniques (9%) or health-related sectors such as biology, biotechnology and motor science university courses (10%). Seven percent of the students had previously enrolled in humanistic disciplines, such as psychology and philosophy. The others students (10%) attended courses in scientific subjects, such as engineering, economics and information technology.nuclei of meaning. To describe the content, they suggested some categories. In this phase, each evaluator added a brief explanation to the category and some phrases extracted from the students’ answers, to highlight the meaning of the category. In the second phase, the two evaluators compared their categories in order to select a common bundle. During the phase three, the evaluators read all the answers again independently, selecting one or more among the categories previously defined for each subject. On average three categories were selected for each subject . Finally, the two evaluators met to compare their selection. The few discrepancies revealed were resolved through discussion. An initial set of 18 categories was developed. Tables The students’ answers to the open question were analysed using qualitative methods . Two indAs described in Table When one compares the frequencies of the different categories with respect to gender and age, some differences are apparent  and the number of the selected categories (m=18) limit the validity of the FA. Some authors have suggested a sample size of at least 200 subjects and ratios of sample size to categories between 5 to 20 [MSAi>.5)  using only 8 categories, the categories best able to identify the quality of the motivation. From the types of controlled motivation, we used the categories JOB, REC, PRA, and FAM. We used the categories EXP, HUM, HEL, and SMI for the types of autonomous motivation.For this analysis, a logistic model with 2 parameters was applied. This model is particularly appropriate for analysing binary data . AccordiThe dependent variable of the logistic model measures the level of internalization of student motivation, in relation to the decision to attend the specific degree course. For each student, the detection of a category with negative slope moves her/him toward a poorer motivational profile, and the detection of a category with a positive slope moves her/him toward a richer profile. Using the estimations of the model, a person-centred analysis can be carried out. This analysis allows for the assignment of a motivational score to each student, according to the pattern of categories extracted from her/his answer.M=0.015,SD=.756). The test of Shapiro-Wilk makes it possible to reject the hypothesis of normality . The estimated value of the skewness, g1=.201(se=.212), highlights the fact that the mass of the distribution is concentrated on the left, i.e. on the controlled types of motivation , with males more controlled with respect to females. A comparison of the density distribution for gender groups shows they are not equivalent (p<.05). The visual analysis of the two distributions highlights that the males are more concentrated around the mean value =2.288,p=.23).Analysing the differences in the scores distribution for gender and age groups reveals some interesting differences. The means are different for females and males ; group B students aged 20-21 (nB=43); group C students over 21 (nC=40). In group A there are novices, i.e. students who have recently graduated. Group B consists of students who obtained their high school degree at least one year ago, in some cases with previous experiences in other degree courses. Group C is more heterogeneous, frequently the members have had previous academic experience. The mean values for the groups, from A to C, are.057 (.792),.107 (.759), and −.186 (.620) respectively (standard deviations in parenthesis). The differences are not statistically significant (F(2.130)=1.901,p=.15). A visual inspection of the density distributions , with more variability in the scores distribution in the younger with respect to older students. Group C shows more controlled and homogeneous scores.From our sample, we extract three age groups with comparable sizes: group A includes students aged between 18-19 years old  [Using the results of the dimensional analysis, we classified subjects into groups with similar motivational profiles and constructed clusters of subjects who are characterised by similar combinations of motivations. The person-centred analysis, according to the study of Vansteenkiste (2009), permits also to verify the external validity of the results of this study. In the study , the aute (SR-S) . The autThese three variables can describe the level of controlled and autonomous motivation for each student, and the total quantity of motivations in her/his answers. To obtain comparable results, a procedure of complete-linkage clustering was carried out. The optimal solution is composed of 4 clusters, with 68% of explained variance, and this result accords well with Vansteenkiste et al. (2009). Comparing the single cluster, some minor differences appear  the variables are strictly correlated. In fact, the authors used linear combination of the item scores in the SR-S to define quality and quantity of motivation. Despite this minor disagreement, it is extremely interesting to highlight how the analysis carried out in this study allows to compare the results of a free-text questionnaire with the outcomes of a structured test, based on strong theoretical assumptions.The examination of motivations that drive first year undergraduate students to choose a university nursing course has been the focus of the present research.In the present study, a set of 18 categories was developed. The categories, obtained applying a thematic analysis to self-reported statements about the choice of nursing degree course, are in line with findings in research literature both in term of typology and relative frequencies –22.This research represents a step forward in the direction of a deep interpretation of students’ motivation. Indeed, it proposes a methodology, organizing the categories within the theoretical framework of SDT. The dimensional analysis carried out in this study has shown that nursing students’ motivations can be organized along one dimension, corresponding to the controlled/autonomous pattern of the SDT. The distribution of the categories reproduces a classification in which there are categories related to controlled motivation , and categories related to autonomous motivation .In a person-centred context, the estimation of the parameters of the unidimensional model permitted us to obtain motivational profiles for students on a nursing degree course. These profiles were in line with the results of other studies that used scales to evaluate student motivation, and were useful in distinguishing between quality and quantity in personal motivation . Three mThe use of the SDT framework has allowed to obtain a coherent interpretation of the students answers and has created the possibility to clusterise the nursing students into categories according to their motivational profiles. This has made it possible to compare the results of our study with studies carried out in other academic contexts and using different tools.The application of the methodology proposed here can permit educators to obtain a more homogeneous vision of the motivations and reasons why students enrol on an academic nursing course. This vision can also help to improve academic policies and learning strategies aimed at dealing with problems related to retention and attrition rates in nursing courses.controlled vs. autonomous motivation.Given the international nursing shortage, with nursing student retention acknowledged as a priority, strategic approaches to enhance their retention are essential. Knowing the reasons for choosing nursing studies is important for educators in order to foster and support undergraduate student motivation. The study of motivation in this context is usually aimed at measuring the quantity of motivation. The application of the SDT framework also allow to take into account the quality of personal motivation, in terms of In different context, some studies show that students with an autonomous profile are more persistent in an academic curriculum than students with other motivational profiles , 34. In Promoting specific interventions and activities to support both lessons and internships, may encourage or reinforce motivational aspects already present in students that are essential for this typology of study and profession. One modality would be to offer opportunities for consultation and confrontation, activities in which students can discuss, analyse and reflect on their continuous study experiences and internships that are provided within the nursing course. Another approach would be to make lessons more interactive, with the use of simulation activities, workshops, discussions, reflections, etc. These activities may facilitate the creation of educational environments that allow students to satisfy their basic needs for autonomy, competence and relatedness, and contribute to retention in nursing studies. Further research is needed to evaluate the efficacy of these autonomous supportive interventions, in terms of modification or preservation of the good quality of motivations.This study has some limitations. To validate the results of this study, further studies in other social and cultural contexts, using also other instruments instead of open questions, such as multiple choice questions, structured interviews, focus groups, etc., are needed.This study represent a first step to better understand the association between the motivational profile of nursing students and learning success and rate of attrition. In fact, intrinsic motivation is expected to be associated with more successful student learning outcomes and success over time, than controlled motivation. Furthermore, intrinsically motivated students will probably persist much more in academic tasks. To investigate the validity of these results following the students along their academic career may be indicated.The cross-sectional nature of this study did not permit to obtain causal relationships between motivations and other situational or dispositional individual traits, such as test anxiety . Only a Another possible further advance of the present study is the opportunity to develop a new quantitative survey, that could be administered to students after their admission to the degree course, which would investigate well known motivational factors for choosing a nursing career within the framework of SDT.Despite the above limitations, this study offers initial empirically based evidence of a common motivational pattern characterising students on a nursing degree course. The SDT appears to be a good theoretical framework to describe and analyse this pattern, independently of individual and contextual differences.This study analysed within the theoretical framework of SDT the answers to open questions aimed at investigating the self-reported motivations of nursing students in the first year of their degree course. It proposed a methodology to overcome some of the biases typical of self-reported data, integrating the qualitative analysis of the nursing students’ statements with multi-dimensional scaling techniques aimed at interpreting the results of the qualitative analysis in the framework of SDT.For this purpose, we analysed the student nurses’ answers using both qualitative and quantitative methods. The qualitative analysis allowed us to collect a large quantity of data about study choices. The aim of this analysis was to identify the main reasons students indicate to motivate their choice of the nursing course. A set of 18 categories was developed. The quantitative analysis utilised the SDT framework to organize these categories along a continuum ranging from controlled to autonomous motivation.Through adoption of a person-centred approach, four motivational profiles were identified: a) students with good quality motivation profile; b) students with poor quality motivation profile; c) students with low quantity motivation profile; d) students with low quantity and poor quality motivation profile.The significance of this study includes the opportunity to understand nursing students’ reasons within the theoretical framework of SDT, a well-grounded model able to offer useful information to educators, in order to plan effective strategies to foster student motivation, to enhance student retention and successful outcomes, to prevent student attrition and prepare students to enter the workforce.


Depression in people with bipolar disorder is a major cause of long-term disability, possibly leading to early mortality and currently, limited safe and effective therapies exist. Although existing monotherapies such as quetiapine have limited proven efficacy and practical tolerability, treatment combinations may lead to improved outcomes. Lamotrigine is an anticonvulsant currently licensed for the prevention of depressive relapses in individuals with bipolar disorder. A double-blinded randomized placebo-controlled trial  was conducted to evaluate the efficacy of lamotrigine plus quetiapine versus quetiapine monotherapy in patients with bipolar type I or type II disorders.Because the original CEQUEL study found significant depressive symptom improvements, the objective of this study was to reanalyze CEQUEL data and determine an unbiased classification accuracy for active lamotrigine versus placebo. We also wanted to establish the time it took for the drug to provide statistically significant outcomes.t test and linear regression. Multiple features were computed from the QIDS-SR16 time series; different linear and nonlinear binary classifiers were trained to distinguish between the two groups. Various feature-selection techniques were used to select a feature set with the greatest explanatory power; a 10-fold cross-validation was used.Between October 21, 2008 and April 27, 2012, 202 participants from 27 sites in United Kingdom were randomly assigned to two treatments; 101: lamotrigine, 101: placebo. The primary variable used for estimating depressive symptoms was based on the Quick Inventory of Depressive Symptomatology—self report version 16 (QIDS-SR16). The original CEQUEL study findings were confirmed by performing P=.09; sample size=81, 77; 95% CI −0.2403 to 3.5036). From weeks 48 to 52, the mean difference was −2.0032 . The coefficient of variation (σ/μ) and detrended fluctuation analysis (DFA) exponent alpha had the greatest explanatory power. The out-of-sample classification accuracy for the 138 participants who reported more than 10 times after week 12 was 62%. A consistent classification accuracy higher than the no-information benchmark was obtained in week 44.From weeks 10 to 14, the mean difference in QIDS-SR16 ratings between the groups was −1.6317 . Bipolar disorder, a psychiatric condition characterized by repeated elevated mood (mania) and low mood (depression) states , has beeThere is, however, no consensus on the effectiveness or safety of antidepressant drugs, such as fluoxetine, for bipolar depression . LamotriThe comparative evaluation of quetiapine plus lamotrigine versus quetiapine monotherapy (CEQUEL) trial was a double-blind randomized placebo-controlled parallel group trial comparing a lamotrigine plus quetiapine treatment and a quetiapine monotherapy treatment in patients diagnosed with a bipolar I or II disorder (EudraCT Number: 2007-004513-33) . A minimThe original analysis reported significant depressive symptom improvements for the lamotrigine subjects compared with the placebo subjects. In this paper, the data collected in the trial were reanalyzed using machine learning approaches with the main objective being the identification of the most appropriate binary classifier to distinguish between the lamotrigine and the placebo effects. In addition to replicating the findings obtained from the original statistical analysis, we also wanted to determine the time it took for the drug to provide statistically significant outcomes to provide some guidance with respect to the minimum amount of time required to undertake trials that aim to establish the treatment or drug efficacy.To assess the differences between patients taking lamotrigine and those taking the placebo, a binary classification approach was used to identify the relevant features to be extracted from the time series. This approach considered the different characteristics in the collected data and sought to classify the participants into 2 distinct groups based on the observed features; in this case, the group taking lamotrigine and the group taking the placebo. The features were determined based on the different statistical metrics computed from the data, and the analysis determined which features would facilitate the classification; for example, it was expected that the mean QIDS-SR16 scores would differ between the 2 groups. For every feature, a kernel based density estimation was used to examine its probability distribution between the 2 groups and to test whether the 2 distributions were different; the bigger the differences, the greater was the explanatory power of the feature. For the same reason, the performance of a classifier for each individual feature was also examined. Finally, the features and classifier with the best cross-validated accuracies were identified.Data were collected from 202 participants over a period of 52 weeks; 149 with bipolar type I disorder and 53 with bipolar type II disorder; of which 90 were male and 112 were female. Initially, there were 202 registered participants. After the first 2 weeks, there was a large drop in the number of people who continued to report; however, there were almost equal numbers of subjects in both the lamotrigine and placebo groups. A more detailed trial profile is presented elsewhere .There was a 60% overall compliance with the self-reported mood ratings at 12 weeks; however, even though fewer participants submitted ratings for the entire 52 weeks, no between group differences were observed. For this reason, 2 data subsets were explored; 153 participants who submitted mood data for at least 5 weeks and 138 participants who reported for at least 10 weeks. Another challenge was that the participants did not submit mood ratings at regular time intervals; therefore, the target frequency of one report per week was not always achieved because patients were able to submit scores at any time during the week, which resulted in unevenly sampled data. As a result, extra care was required when using the general time series techniques that had been originally developed for uniformly sampled data.Because the overall goal was to build a binary classifier that could differentiate the patients taking the lamotrigine from those taking the placebo, the first exploratory step was to study the different statistical metrics, called features, that were calculated using the dataset with the aim of identifying the “good” features to feed into the classifier, that is, those features that had sufficient explanatory power to facilitate the classification task. Therefore, how each feature contributed to the classification accuracy when used individually was also investigated.A common approach when evaluating the explanatory power of a feature has been to assess its associated probability distribution. Kernel smoothing density estimation ,13 is a A receiver operating characteristic (ROC) curve is a graphical plot that shows the classification threshold variations of a binary classifier. Given a labeled dataset, a binary classifier is able to produce the following 4 results from the comparisons of the predicted class to the original labels: true positives, false positives, true negatives, and false negatives. Because the classification threshold is varied, the ROC curve plots the true positive rate versus the false positive rate with the area under the (ROC) curve providing the single metric for the evaluation of the classification model; the larger the area under the curve, the greater the possibility of realizing a high true positive rate and a low false positive rate.1 and L2 norms of the error obtained on a cross-validation set. With these techniques, the best performing features were added to the model one by one up to the point at which the cross-validation error did not reduce. On the other hand, sequential feature selection techniques work in a similar manner but use the misclassification rate as an error metric. We studied both techniques for feature selection to test the robustness of our results.In practice, using one single variable or using all available variables for the classification may not result in an optimal classifier. Various feature selection techniques can be used to select the best model from among the set of available variables, which involves selecting those variables that are representative of the data variability, while improving the classification accuracy. Two types of techniques for feature selection were considered. On one hand, lasso, elasticnet and ridge regression  were selThe different variables computed from the original dataset were investigated as candidate features to feed into the classifiers. The raw dataset contained different subject attributes, such as demographic information (age and gender) and bipolar type, and reported QIDS-SR16 and ASRM values. The age, gender, and bipolar type were kept as they were, and a range of statistics was computed from the QIDS-SR16 values. The ASRM values were not investigated further as these did not indicate any valuable information in the early exploratory stage. Further, because the lamotrigine was being used to treat bipolar depression, the QIDS-SR16 values offered greater information. The QIDS-SR16 mean and SD were the initial candidate features, that is, if the drug worked, the mean QIDS-SR16 value would be lower for the subjects taking the lamotrigine than for those taking the placebo. The coefficient of variation, which is defined as the ratio of SD to the mean, was also used as a separate feature. Other simple statistics considered were the skewness and kurtosis in each subject’s QIDS-SR16 time series, which summarized the QIDS-SR16 data distribution but did not reflect the temporal dynamics.f ≤0.2) power and the sum of the high frequency (f >0.2) power. The ratio between the amount of power at these high and low frequencies was also calculated as an additional feature.In addition to these basic statistics, other more complicated features were computed from the QIDS-SR16 time series data. The Lomb periodogram ,16, alsoDetrended fluctuation analysis (DFA)  is anothTo maximize the classification performance, a number of linear and nonlinear classifiers were also investigated. A linear classifier is a classification algorithm, the objective function of which is a function of a linear combination of features. A binary linear classifier has a linear decision boundary, and a nonlinear classifier has a nonlinear decision boundary. Detailed algorithmic procedures for the classifiers used here are not given and interested readers can consult the relevant references. The linear classifiers investigated were logistic regression , linear In short, the classification performances were evaluated using the different linear and nonlinear methods for each of the individual features and the subset of features obtained in the feature selection step. However, when using complex nonlinear models, it is relatively easy to overfit the data; therefore, to avoid this problem, the evaluation metric was based on the classification accuracy obtained from the out-of-sample data for which 10-fold cross-validation was used to evaluate the out-of-sample performances. The construction, evaluation, and comparison of the many different models for the several features involved an exhaustive search and comparison of the performance of the quantitative classifiers. In reality, because there was no perfect model, the more these different models agreed with each other, the more confidence there was in the results. Parsimonious models are attractive not only because the risk of overfitting is reduced but also because the simpler the model, the easier it is to interpret the results and improve understanding. Data from one, two, and three quarters of the trial period were also considered so that the performance of the classifier could be monitored over time to assess the optimal trial durations for the lamotrigine and placebo comparison.The initial data exploration analyzed the trends in the QIDS-SR16 time series. The probability distributions for the reported QIDS-SR16 and ASRM values were also investigated. The kernel density estimates shown in t test on the mean QIDS-SR16 scores of each participant were performed for different 4 week periods, and the results are summarized in P<.001).The results in After designing many features to feed into the classifier, feature selection was conducted using lasso, elasticnet, ridge regression, and the sequential feature selection techniques. All these methods agreed on the choice of only 2 variables, the scaling exponent alpha from DFA and the coefficient of variation (σ/μ). The performance evaluations for these 2 features are summarized in These 2 selected features produced easily understood information. The coefficient of variation (σ/μ) reflected the shape of the QIDS-SR16 distribution and was not affected by the weekly fluctuations, and the DFA exponent alpha quantified the nature of the temporal weekly fluctuations and was not affected by the distribution. Therefore, intuitively, the coefficient of variation (σ/μ) was seen as a standardized measure of the dispersion and the DFA exponent alpha as a measure of the temporal stability.Because the chosen model only had 2 variables, it was easy to visualize the classification decision boundary. Given the linear separation of the dataset, it was also possible to visualize the time series for the participants lying in the 4 decision area corners; A binary classification analysis was conducted, for which a variety of linear and nonlinear models were applied to the individual features, all features, and to the 2 selected features . The results are summarized in th to the 52nd weeks). The maximum of the fractions for the lamotrigine and placebo participants served as the “no information” benchmark for the classifier. In addition, for most periods from 20 weeks onwards, the classifier was outperforming the benchmark. For trials ranging from 44 to 52 weeks, the classification accuracy was greater than the benchmark and was statistically significant.As mentioned, participant compliance was important and was found to yield time series of varying durations. A clinician who was using the classifier to test whether lamotrigine was better than the placebo would need to know how many weeks of data were required before a definitive decision could be made. For this reason, the performance of the logistic regression model was evaluated against the amount of data reported by participants. For this evaluation, the participants were selected if they had provided at least 10 QIDS-SR16 responses for trial durations of 13 to 52 weeks. The results from the classification model confirmed the original findings of the CEQUEL study that the addition of lamotrigine to quetiapine for the treatment of severe bipolar depression decreased depressive symptoms compared with a placebo. Although the original CEQUEL data analysis had relied on linear regression model fitting, the classification model used in this study not only examined the data from a different perspective but also provided robust explanatory power because it allowed for an estimation of the extent to which the 2 groups of observations  could be distinguished based only on the QIDS-SR16 scores. Therefore, a model was constructed without the need for any prior information about the clinical interventions during the treatment period. Using out-of-sample classification accuracy and simpler models allowed us to avoid overfitting that is problematic when employing complicated machine learning models on shallow datasets. Another advantage of the machine learning models was the ability to test multiple features computed from the raw data and select only those features that had sufficient explanatory power.The use of DFA on the QIDS-SR16 time series allowed us to examine the temporal stability of the treatment, an aspect that was not considered in the original study. Future data analyses could attempt to explain why the QIDS-SR16 scores of the participants taking the active lamotrigine were generally temporally unstable. The effect of other clinical interventions and concurrent treatments during the study period could also be considered. Finally, it was demonstrated that machine learning techniques could be generally used in clinical trials to provide greater insights into what the data represent beyond classical statistical analyses, especially when there are large, complex datasets available. One drawback of using machine learning techniques, however, is that the analyst must deal with the bias-variance tradeoff. Another disadvantage is that some powerful machine learning models require very large datasets to be able to generalize well.This study confirmed that the use of lamotrigine decreases depressive symptoms in bipolar patients. The selected classification features suggested that lamotrigine increased the coefficient of variation (achieved by increasing SD or decreasing the mean of the QIDS-SR16 time series). It was also found that patients taking lamotrigine tended to have rougher time series, which was indicative of a greater temporal instability in the time series. The 2 features, the coefficient of variation and DFA exponent, implied that a two-dimensional visualization diagram and linear decision boundary can be constructed to better understand bipolar disorder and the ways that the participants are affected by lamotrigine. The statistical significance of the classification was evaluated, from which it was determined that a trial of at least 44 weeks was required to distinguish between lamotrigine and the placebo. It would be useful to conduct additional studies to obtain a larger cohort of compliant participants. The selected features provided a deeper understanding of the temporal dynamics of subjects experiencing bipolar disorder and offered the potential for the better monitoring of symptoms over time.


Foot-mounted Inertial Pedestrian-Positioning Systems (FIPPSs) based on Micro Inertial Measurement Units (MIMUs), have recently attracted widespread attention with the rapid development of MIMUs. The can be used in challenging environments such as firefighting and the military, even without augmenting with Global Navigation Satellite System (GNSS). Zero Velocity Update (ZUPT) provides a solution for the accumulated positioning errors produced by the low precision and high noise of the MIMU, however, there are some problems using ZUPT for FIPPS, include fast-initial alignment and unobserved heading misalignment angle, which are addressed in this paper. Our first contribution is proposing a fast-initial alignment algorithm for foot-mounted inertial/magnetometer pedestrian positioning based on the Adaptive Gradient Descent Algorithm (AGDA). Considering the characteristics of gravity and Earth’s magnetic field, measured by accelerometers and magnetometers, respectively, when the pedestrian is standing at one place, the AGDA is introduced as the fast-initial alignment. The AGDA is able to estimate the initial attitude and enhance the ability of magnetic disturbance suppression. Our second contribution in this paper is proposing an inertial/magnetometer positioning algorithm based on an adaptive Kalman filter to solve the problem of the unobserved heading misalignment angle. The algorithm utilizes heading misalignment angle as an observation for the Kalman filter and can improve the accuracy of pedestrian position by compensating for magnetic disturbances. In addition, introducing an adaptive parameter in the Kalman filter is able to compensate the varying magnetic disturbance for each ZUPT instant during the walking phase of the pedestrian. The performance of the proposed method is examined by conducting pedestrian test trajectory using MTi-G710 manufacture by XSENS. The experimental results verify the effectiveness and applicability of the proposed method. Pedestrian Position Systems based on Micro Inertial Measurement Unit (MIMUs) have recently attracted widespread attention with the rapid development of MIMUs. Foot-mounted MIMUs (including gyroscopes and accelerometers) can measure the acceleration and angular velocity of pedestrians in real time. These measurements are utilized to estimate velocity and position. MIMUs are self-contained fully autonomous systems, immune to external interference. They can be used in Global Navigation Satellite System (GNSS)-denied environments, and are very beneficial in firefighting and military applications ,2. HowevInstalling the MIMU on the pedestrian foot is called Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) ,6,7,8. AThe first problem for FIPPS is the initial value setting of the dead reckoning algorithm. The principle of dead reckoning is that the increment  is obtained by the MIMU measurement. Then, the navigation information could be calculated by the sum of the initial value and the increment. It is assumed that the pedestrian’s initial state is standing at one point, so the initial velocity value is zero. The initial position can be obtained by GNSS or set to zero. The difficulty for FIPPS is the determining the initial attitude. The process of obtaining the initial attitude is called initial alignment. To acquire the initial alignment for a high precision Inertial Navigation System (INS), it will be assumed that an accelerometer will measure only gravity, a gyro will measure only the Earth’s rotation angle velocity ,17. HoweThe second problem for FIPPS is the observability of ZUPT. ZUPT can reduce the position error, velocity error, and horizontal misalignment angle of the system, but the heading misalignment angle is unobserved, which results in a continuous increase in the heading error and an increase in the positioning error. The heading errors can be corrected by augmenting MIMU with other sensors ,25. MagnTo address the limitation of correction schemes using the magnetometer discussed above, an improved method for foot-mounted inertial/ magnetometer pedestrian positioning based on adaptive gradient descent algorithm is proposed in this paper. This paper consists of the following sections: For FIPPS, the MIMU is installed on the pedestrian foot, and the acceleration and angular velocity of the pedestrian foot are measured in real time. Then, the pedestrian positioning information is obtained through the dead reckoning method . The cork; k; n frame; n frame.Here, subscripts From Equation (1), it can be obtained that setting the parameters of shown in  will be The low-cost MEMS-based MIMU sensors have high noise and composite error characteristics. The overall positioning accuracy deteriorates due to the measurement error of the The ZUPT scheme is utilized to address this problem ,39,40. CThen, based on this observation, the Kalman filter is used to estimate each navigation error to reduce the positioning error caused by measurement noise of inertial components ,42. The It can be found that matrix Theorem 1.If the following condition can be satisfied, a linear time-invariant system is a completely observable system:where, is the dimension.According to the theorem, it can be calculated that the rank of the matrix is 5 and five variables of ferences ,44, the The purpose of initial alignment for pedestrian alignment is to obtain the initial attitude within a few seconds. It is hard to finish the initial alignment only using MIMUs themselves because of their high measurement noise. Hence, introducing the magnetometer as an external sensor to the initial alignment is proposed in this paper. Madgwick  proposedHowever, the precondition of introducing GDA for initial alignment is that the accelerometer and magnetometer errors are neglected . The meak, k − 1 respectively. n frame, The Adaptive Gradient Descent Algorithm (AGDA), which is used to enhance the ability of magnetic disturbance suppression, is proposed to the fast-initial alignment for pedestrian position system. The diagram of AGDA is shown in According to the Madgwick . If therThe optimal solution of the quaternion is obtained by Equation (3). The transformation matrix of After initial alignment, the magnetic disturbance at the pedestrian standing point is calculated based on the initial alignment result Two key advantages of introducing AGDA as the fast-initial alignment algorithm are: (1) the magnetic disturbance can be estimated and compensated in a short time; (2) the initial attitude can be obtained by the accelerometer and magnetometer measurement without magnetic disturbance. Therefore, the AGDA can be used as the initial alignment algorithm, but it cannot be used in the ZUPT during the pedestrian walking period. Because the duration of time foot touches the ground while walking is about 1 s or shorter.To calculate the uncertainty of the AGDA, the difference between the earth’s magnetic field and the magnetometer measurement after compensation can be introduced as the criteria. Equation (5) is the calculation formula of the AGDA uncertainty:For the AGDA, it is assumed that the Earth’s magnetic field at the same place is constant. Hence, if both initial alignment result According to the principle of magnetometers, the heading can be obtained by the magnetometer measurement when the magnetic interference is corrected. Then, for the Inertial/Magnetometer Positioning system, the heading information can be calculated in real time when the magnetic interference is estimated during the initial alignment process. However, the magnetic interference can be inconsistent at different points of the trajectory. For example, magnetic fields produced by a power line along the walking trajectory will change the magnetic disturbance. Hence, the magnetic disturbance at ZUPT point during the pedestrian walking trajectory is different from that at an initial alignment point. This section discusses the important issue of how to estimate and reduce the magnetic disturbance.Based on the observation analysis in Here, the difference between the The geometric relation of Earth’s magnetic field, magnetic disturbance, and magnetometer magnetic  measured by the magnetometer is shown in Here, According to the geometric relation in The heading The heading misalignment angles However, the premise of solution above is that the magnetic disturbance Here, The adaptive parameter When the magnetic disturbance at ZUPT point is different from the initial alignment point, the magnetic projection measured by the magnetometers along the According to the traditional pedestrian positioning method mentioned in The blue boxes in To verify the correctness and effectiveness of the improved method for foot-mounted inertial/ magnetometer pedestrian-positioning system based on AGDA in this paper, MTi-G710 MIMU manufactured by XSENS  is used as the footwear test equipment for walking experiments. The position and the orientation of the MIMU connected to a shoe are shown in Two test trajectories were carried out. Trajectory 1 was conducted to test and verify the validity of the algorithm proposed in this paper. For the first trajectory, an “8 shape” path was chosen, and there was no change in the magnetic interference throughout this trajectory. The second trajectory was conducted around the buildings to test the stability of the improve ZUPT algorithm, especially in the changing magnetic interference scenarios. Building proximity introduced many variable factors to influence the magnetometer interference, which included electric cables, metals, etc.Test 1Basic ZUPT: navigation algorithm of the basic ZUPT algorithm is discussed in Fast-initial alignment by AGDA: ADGA initial alignment algorithm proposed in Basic ZUPT + AGDA: the ZUPT algorithm based on the AGDA fast-initial alignment algorithm in Improve ZUPT: the ZUPT algorithm based on the fast-initial alignment algorithm by AGDA was introduced in GPS Position: GPS position as the standard reference information.The first pedestrian trajectory conducted for testing the proposed algorithm is an “8 shape” path. Pedestrian remained standing for 7 s to attain initial alignment attitude at the beginning of the test. The curves of left column in From the curves in The output of the MTi-G710 magnetometer is in arbitrary units (a.u.), where one a.u. is the magnetic field strength during calibration at the Xsens calibration lab. Hence, there is no unit on the y-axis in From The pedestrian position result is shown in Test 2Basic ZUPT: the basic ZUPT algorithm is discussed in Alefa Zero: ZUPT algorithm based on the adaptive positioning algorithm is discussed in Improve ZUPT: ZUPT based on the fast-initial alignment algorithm by AGDA in GPS Position: GPS position as the standard reference information.To test, whether the improve ZUPT algorithm proposed in this paper can be used in the changing magnetic interference environment, three test trajectories were carried out. The walking paths are rectangle, circle, and irregular shape respectively. According to the magnetometer principle, it can be obtained that the sum of the magnetometer measurement vector along three axes is constant if there are no magnetic disturbances. The magnetic vector sum will change when a magnetic disturbance appears. In addition, the output of the MTi-G710 magnetometer is processed in arbitrary units (a.u.) in XSENS’s lab. Therefore, the difference between the vector sum of the magnetometer measurement along three axis and 1 is the magnetic disturbances during the experiments. The differences are shown in The position error is reduced by estimating the heading misalignment angle and using the adaptive Kalman Filter proposed in this paper, hence, the trajectory tracked and the heading estimation results are the two main states to reflect the performance of the algorithm proposed in this paper. According to the test results of different walking paths, it can be observed the position accuracy of the proposed adaptive inertial/magnetometer positioning algorithm is better than basic ZUPT and Alefa Zero ZUPT algorithms, and the trajectory can be tracked better. However, the trajectory positional tracking result is worse as compared to the test 1 because the magnetic interference is changing in test 2, and only part of the magnetic noise can be compensated by the adaptive Kaman Filter. The unobserved magnetic noise leads to the accumulating heading error and the position error. An improved method for foot-mounted inertial/magnetometer pedestrian positioning based on an adaptive gradient descent algorithm is proposed in this paper. According to the principle of foot-mounted inertial pedestrian positioning, this paper presents a fast-initial alignment algorithm based on AGDA, where the initial attitude and the magnetic disturbance can be estimated during the pedestrian standing period by introducing the AGDA as the alignment algorithm. To solve the problem of the heading unobserved misalignment angle, this paper presents an adaptive inertial/magnetometer positioning algorithm. The magnetic disturbance is compensated, and the heading misalignment angle is used as an observation, therefore the accuracy of pedestrian position is improved. In addition, to compensate the change in the magnetic disturbance for ZUPT an adaptive parameter in the Kalman filter is added to enhance the positioning solution. MTi-G710, which is produced by XSENS is used to verify the proposed technique. The experimental results positively demonstrate the effectiveness of the proposed method for pedestrian-positioning in this paper.


Existing evidence on the impact of foreign direct investment on domestic economies remains ambiguous. Positive technology spillovers of foreign investment may be outweighed by negative crowding out effect due to increased competition. In this paper, we employ a unique country/sector-level data set to investigate the impact of what is considered the ‘best’ type of foreign investment—greenfield knowledge intensive FDI—on domestic entry. Our results suggest that, in the short run, this type of FDI is positively related to the entry rate in the host country, if the domestic sector is either dynamic, or highly R&D intensive. These sectors may be respectively characterized by lower entry costs, which encourage a ‘trial-and-error’ learning business approach, and by a higher level of absorptive capacity which increases the chance of technology transfer.The online version of this article (doi:10.1007/s10961-017-9575-y) contains supplementary material, which is available to authorized users. Foreign direct investment (FDI) is expected to transfer knowledge and technology, enhance productivity, competitiveness, and ultimately boost long-run growth of the domestic economy. Many studies have investigated what factors and government strategies attract such type of investment Guimón , and whaMost of the relevant literature looks at the impact of FDI on proxies of economic growth such as capital accumulation, total factor productivity (TFP) growth, and gross domestic product (GDP) growth. More recently, an increasing number of studies deals with the relationship between FDI  and new local firm’s entry or firm’s survival  online databaseIn this paper, we use a novel data set on greenfield FDI from the fDi Market  of Eurostat, and industry level information from OECD databases.The fDi Markets is an on-line database maintained by fDi Intelligence, a division of the Financial Times Ltd. Since 2003, fDi Intelligence collects available information from company data and media sources on greenfield foreign direct investments and monitors cross-border investments covering all sectors and countries worldwide. Data from the fDi Markets database are used by the UNCTAD to present global investment trends in the World Investment Reports seriesOur second data source is the structural business statistics (SBS). The SBS is a database on firm dynamics maintained by Eurostat, which covers, among others, the number of firm births, the number of firm closures and the number of active enterprises in the EU member states. The data are available for the EU-28 countries since their respective entry into the EU. As with all other statistics from Eurostat, the data for the SBS are assembled by statistical agencies of member states and originate in the national business registers. The national statistical offices collect the data based on internationally harmonised rules for data collection and preparation. The data are available at the sector level. Based on this information firms can be classified into different subgroups according to their sector affiliation.In order to control for factors that are found to be relevant for the formation of new firms at the sector level we make use of two databases from the OECD. The first is the OECD Main Science and Technology Indicators database (OECD-MSTI) which provides information on the activities of the OECD countries in the field of science and technology such as R&D expenditures. The second is the database for Structural Analysis (STAN) which includes measures of output and inputs at the industry level. From this database, we extract the value added, the gross operating surplus and mixed income, the gross capital stock, the labour costs, the number of employees and the production value.Our data sample covers the period between 2005 and 2012. The focus on this period is mainly due to the availability of the relevant data at the time of the analysis. Because we lag our independent variables by one year in order to avoid simultaneity bias, the data on the cross-border greenfield projects from the fDi Markets database as well as the aggregate production and R&D statistics from the OECD cover the period 2004–2011.Matching and integrating heterogeneous data sources allows us to analyse the impact of knowledge intensive greenfield FDI on the local entrepreneurship; it presents, however, several challenges. One above all is the very small matched sample size (45–88 observations) deriving from the different patterns of missing observations across the various data sets. To mitigate this problem, we decided to impute the missing values of the total number of active firms in the SBSTable Our focal right-hand-side variable is the foreign entry rate; it is defined similarly to other studies  and on the economic growth of the domestic industry , capital intensity (CapInt), the firms churn rate dummy (Dyn), and the level of technology (Tech). To investigate the impact of greenfield foreign entry on domestic entry, we regress the domestic entry rate of sector Dyn, a dummy taking value of one if the sector has a churn rate above 15% (dynamic sector), and zero otherwise (static sector)The churn rate dummy, Tech, a dummy taking value of 1 if the sector is a medium/high-tech one, zero otherwiseThe technological classification of the sector, Given the ambiguous results found in the literature concerning the sign of the impact of foreign entry, we test whether this coefficient vary by sector classification. In particular, following some of the predictions in the literature, we interact the greenfield foreign entry variable with:Our data set is an unbalanced panel with observations clustered by country, sector and year. Our regression analysis is based on generalized least square (GLS) accounting for panel heteroskedasticity (the variance varies for each country-sector pair). All control variables are lagged by one year to potentially correct for issues of reverse causality. Nonetheless, the endogeneity deriving from the characteristics of the host market that affect the choice of FDI location and type of investment may yield inconsistent results. Figure We account for these potential endogeneity biases and rely on the instrumental variable approach. Namely, we use a 2-stage least squares (2SLS) estimator. In line with Danakol et al. , we use Table Column 2 displays the regression coefficients from a full specification that takes into account the past domestic new entry rate, sectoral knowledge capabilities, physical capital intensity, and the profitability and the growth of sectors’ output. The marginal effect of past foreign investment in knowledge intensive activities on new domestic entry is not significant when taking these factors into consideration.On the one hand, our results differ from those of Colantone and Sleuwaegen  and De BThe coefficient measuring the impact of industry capital intensity  on entry rate has the expected negative sign. In particular, while capital intensity affects positively the probability of firms’ survival , (4) and (5) we include the interaction between knowledge intensive foreign entry and the firm churn rate dummy (3), the technological level of the sector (4), and both (5). When interacting the knowledge intensive foreign entry with the business churn dummy, we find that as foreign entry rate increases, the domestic entry rate of dynamic sectors increases 8% faster than that of static sectors. Higher industry turnover is typically associated with lower barriers to business. This, in turn, attracts both domestic and foreign businesses. Contrarily, static industries may be characterized by older incumbents and higher entry costs. In these industries, MNEs have larger capital to face the barriers to entry and would eventually discourage the entry of local businesses. Our results contrast the “surprising” positive effects in static industries found by Bürke et al. , p. 403;In column (4), we include the interaction of knowledge intensive foreign entry rate and technology intensity.These results are also in line with those of Görg and Strobl , who argFinally, in column (5), we consider both the interactions with the sectoral turnover and the technological intensity. The results confirm that the new foreign knowledge intensive entry corresponds to 6.5 and 16% faster new domestic entry rates in dynamic and high/medium-tech sectors, respectively. The marginal effect of greenfield FDI is non-statistically significant.ForEntry is regressed on the logarithm (and squared logarithm) of the weighted geographical distance between the investing origin countries and the destination country. We also report a set of test statistics to assess the goodness of fit  is an essential part of an international economic system and potentially a major catalyst for economic development. The catalytic potential arises from the fact that FDI is usually accompanied with a transfer of technology and knowledge from the country of origin to the host country. In particular, this applies to knowledge intensive FDI where R&D activities are outsourced to another country than the country of the headquarter.Yet, the benefits from FDI do not accrue automatically and evenly across countries and sectors. The main reason is that most of the knowledge is not codified but tacit. Thus, some transformative mechanism is needed to ensure that the knowledge transfer actually takes place. One such mechanism is the start-up of new firms. Individuals might be inspired to new business ideas by the knowledge existing in foreign firms and set up a new firm thereby transferring the knowledge from the foreign firm to the local economy.In this paper, we constructed a unique country/sector-level panel data set to analyse how knowledge intensive greenfield FDI affects domestic economy, looking at the impact on the generation of new local businesses. Our main contribution to the literature on FDI spillovers is that we explore the empirically underinvestigated relationship between greenfield FDI and domestic entry. Compared to previous studies that rely on the largely available information on M&A, we build our analyses on the less explored data on greenfield knowledge intensive FDI, which is considered to be the ‘best’ type of FDI with the highest potential of positive effects for the host country.We find that the short-run impact of this type of FDI on domestic entrepreneurship is generally positive as one would expect from greenfield knowledge intensive investment, and that the benefits from this type foreign entry are larger in more dynamic and technologically intensive sectors, than in static and low-tech sectors. The observed positive effects for dynamic and high- and medium-tech sectors validate some of the hypotheses advanced by the literature on knowledge spillover theory of entrepreneurship . This implies that we cannot distinguish between vertical and horizontal spillovers, and therefore cannot map the linkage structure. Third, as discussed in Sect. Nevertheless, we think that some policy implications can be derived from our results. Based on the positive spillovers expected from greenfield or knowledge intensive foreign investment, national governments have adopted several competitive strategies to attract FDI, often racing to bottom of labor and fiscal standards Javorcik . While tFurther research efforts should explore the long-run effects of knowledge intensive FDI, the role of country-specific institutions, and the type of entrepreneurship that is affected by foreign investment. Our future research agenda therefore includes the analysis of greenfield FDI and its impact on different types of start-ups, controlling for information on knowledge infrastructure at the regional level.Supplementary material 1 (pdf 43 KB)Below is the link to the electronic supplementary material.