The aim of the study was to explore mothers’ experiences of having an infant born prematurely (28–32 weeks gestation). In particular, the study aimed to explore the developing parent–infant relationship 12–30 months since birth and the developing parental identity during hospitalization and discharge.Twelve mothers, aged between 22 and 43, participated in the semi-structured interviews. The mean age of infants was 19 months. Interviews comprised open-ended questions and visual stimuli consisting of photographs brought by participants, word selection, and card sorting techniques. Data were analyzed using Braun and Clarke’s thematic analysis .Three themes arose from a clustering of 10 subthemes: (a) Emotional Impact, (b) Searching for Parent Identity, and (c) Moving Beyond Adversity. Participants expressed experiencing heightened emotional distress during the time of their infants’ birth and hospitalization and initially not feeling like parents. Their parental identity strengthened as they became more involved in the care of their infant and began to accept the situation. Participants described parenting their premature infants differently compared with parents of full-term infants, and described adjusting to this difference over time.The findings highlight the emotional experience and adjustment of mothers of premature infants, from hospital and postdischarge. The need for psycho-educational interventions postdischarge and parent-partnered models during hospitalization is discussed. In addition, the study demonstrates the use of integrating visual stimuli in qualitative data collection procedures, to elicit further meaning and interaction from participants with the interview process.  Premature birth is defined as infants born alive before 37 weeks gestation. Infants born between 28 and 32 weeks are considered to be “very premature” and under 28 weeks is coined “extremely premature” . Compared with full-term born infants, those born prematurely are at increased risk of illness , which can result in longer hospital stays, future hospital admissions, and long-term congenital conditions . The iniSeveral studies have focused on the psychological effects that having an infant born prematurely and spending time in the NICU has on parents. Results examining parental stress show that stress levels are higher in parents whose infants had lower gestational ages and birth weights compared with parents of full-term infants , and thaFewer studies have focused on parents’ subjective experiences following discharge from hospital and how the transition impacts on their parent–infant relationship and identity. As the research questions focus on exploring primary caregivers’ subjective experience of their parenting journey, a qualitative research design using semi-structured interviews was adopted. The interview guide was based on the research questions and developed through consultation within the research team and reviewing previous interview questions in the area . The firCriterion sampling was used for participant recruitment. Inclusion criteria consisted of (a) primary caregivers over 18 years of age of an infant born between 28 and 32 weeks’ gestation (inclusive of infants born during 32nd week gestation); (b) at birth, infant spent time in the NICU; (c) at interview, infant was between 12- and 30-months old; and (d) infant is being co-parented. Exclusion criteria were (a) infant born as part of a multiple birth; (b) having major congenital anomalies/birth defects such as cerebral palsy, heart defects, learning disability syndromes, neural tube defects, congenital hydrocephalus; (c) main caregiver currently attending or seeking mental health services; and (d) main caregiver having more than one child born prematurely. As infants born before 28 weeks tend to be more at risk of having disabilities or congenital anomalies ; this wawww.tinylife.org.uk). TinyLife provides free services within NICU’s and upon discharge to all parents living in Northern Ireland who have a premature infant and can be accessed easily by all types of families. The recruitment documentation was developed after review with TinyLife staff. An invitation to participate in the study, including a link to the information sheet, was posted at the end of May 2019 within TinyLife’s Twitter and Facebook accounts. Twelve parents contacted the research team via email expressing an interest in participating. A final round of recruitment took place at the start of August 2019 through the same means. Eight further parents contacted the research team. Telephone screening to discuss eligibility, informed consent, and the purpose of the study was conducted by the lead researcher (C.S.) who also conducted the interviews. This helped to build the relationship with potential participants prior to data collection and introduce them to the interviewer. The lead researcher was female, had no children, was employed as a Trainee Clinical Psychologist, and had previous training in research methods and data collection. Overall, five parents who met exclusion criteria were informed that they were unable to participate, and three parents did not respond to correspondence. Interviews were conducted by the lead researcher between June 6 and November 7, 2019 with parents who met inclusion criteria and who consented to participate (n = 12). Details of the interview, information leaflets, consent forms, and instructions regarding photographs to bring were sent to participants prior to the interview. The photograph instructions asked participants to bring two recent or past photographs that represent their premature birth journey which could include people, objects, places, or relevant images. A separate consent form was provided to participants detailing how their photographs would be used in the research and anonymized. Participants were given the option to conduct the interviews in university premises or in their homes. Five interviews took place in participants’ homes and seven took place in university premises. Within the university, interviews took place in a consistent location. Participants were asked for their infant not to be present for the interview. Prior to audio-recording, the interviews using a dictaphone, participants were asked to provide written informed consent. Participants understood that they could end their participation at any time without this affecting their care or involvement with TinyLife. Interviews were administered in an empathic style using open-ended questions (see www.cuddlebright.com).Participants were recruited via the social media platforms of TinyLife, a premature and vulnerable baby charity based in Northern Ireland . Participants were living in Northern Ireland at the time of the interviews and spoke fluent English. Their infants consisted of five females and seven males with a mean gestational age of 30 weeks (SD = 1.62) and a mean hospital stay of eight weeks (SD = 3.65). At interview, the infants ranged in age between 12 and 28 months . Demographics are summarized in Emotional Impact, (b) Searching for Parent Identity, and (c) Moving Beyond Adversity, which arose from a clustering of 10 subthemes Most participants spoke of memories of leaving their infants in the hospital, which brought heightened distress that has persisted over time; “saying goodbye, I don’t think that is ever going to leave me just how hard it was to leave him in the hospital […], it’s just awful” (Jill). In addition, participants mentioned specific symptoms linked to a trauma response, such as flashbacks and recurring nightmares; “like every night you know [I had] a visual nightmare where I would wake up with the sweat and the tears and was up and standing around at her funeral” (Lauren).There was a felt sense of responsibility for some participants regarding the health of their infant which they associated with feelings of distress; participants felt guilty of the harm they believed to have caused them and wondered what they had done. Participants also described finding it difficult to forgive themselves due to the self-blame they felt; “I think I’ll never fully forgive myself and I’ll always fully think there was something I did or didn’t do, that will always be at the back of my mind” (Jane).I’ve felt like the whole thing was my fault […], so the first time that I held her […] I actually said to her ‘I’m sorry’ [crying] ‘cause I felt it was my fault, because it was my body that rejected the placenta and everything else (Mia)Distress was also mentioned regarding the medical environment, such as unhelpful staff responses, perception of the environment as intrusive, and medical procedures occurring quickly.She took me to about three other machines and then she got the consultant and he literally straight away said ‘yep, this needs to come out’ […]so he was delivered that night at twenty to ten by emergency section and I really wasn’t prepared for it” (Jess)This subtheme was linked to participants initially feeling unprepared for the experience and feeling fearful of what may happen to their infants in the present and future; “I think it was absolute terror whenever I found out that she was coming early it was like, I’m not ready for this, I have no clothes, I have no cot” (Mia).Having an infant born prematurely caused much fear in participants regarding interacting with the infant and due to future thoughts of the potential difficulties that the infant may have; “I suppose when Megan was growing up, we were always worrying ‘would she meet this milestone, would she be developmentally okay?’” (Kate).I remember the lady squirting hand sanitiser into my hand and saying, reach in and touch your baby, [..] and I said ‘no I can’t, I’m going to hurt her’ and I remember being frightened because she was so tiny (Helen)Participants described feeling different from their peers and feeling unable to connect with parents of full-term infants.I got into arguments with a lot of people, like my mum and my sister […] just after he was born and it was because I think ‘you don’t understand what I’m going through, you don’t know what it’s like’. I know my sister has kids but it’s not the same (Jane)Feeling different was linked to loss. Participants had preformed ideals of what parenthood would be like, which they felt they were deprived of and had lost; “this [photograph] is his first car ride. So obviously whenever you think of your baby coming home you think your first car ride is going to be in your car seat... His was in an ambulance” Participants realized that their life was different from where they thought they would be once they had a new-born. This sense of difference created a dichotomy between the present situation and what could have been, contributing to feelings of being different.What I most looked forward to when I got home, was to be able to sit in the house in my pyjamas instead of getting up, getting ready, and going to hospital… so I feel like my whole maternity was completely different from a full-term baby (Lisa)Within this subtheme, participants described experiences where they were directly faced with the medical conditions of their infant and associated these experiences with feeling fearful. They also described experiences of anticipating their infants’ health deteriorating and their potential death; “I got [my partner] to take a video because I thought ‘if she passed away, at least I would have seen her move and be alive’” (Helen).Participants hoped for a positive outcome but reported having constant reminders of the vulnerability of their infants. Participants also described their own physical and psychological frailty; “I was very sick after Megan was born. I had to have the drip on because they thought I was going to have seizures, so I didn’t get to see her for a full 24 hours ” (Kate). Although none of the parents in the sample were attending mental health services, several of them described believing they had suffered from mental health problems such as postnatal depression, post-traumatic stress disorder, or an anxiety disorder;I feel like even now, I’m just getting back to who I was before Layla was born, although I was never diagnosed with any post-natal depression or anything like that, I think there was an underlying thing somewhere within me (Mia)Participants described an initial sense of not feeling like they were parents while in hospital due to the dependence on the medical system to keep their infants alive and feeling a loss of their parenting role. This was also related to not being responsible for performing basic care tasks and feeling like they were unable to initially strengthen their emotional bond with their infant. Participants contrasted their actual lived experience to what they had previously envisioned and hoped for; “I felt like I was still pregnant, but I wasn’t carrying him. The incubator is like my belly. I just left him off somewhere to be, to do the rest of the job, but I didn’t feel like I was a parent” (Ava).They’re keeping her alive and warm. She’s wrapped in bubble wrap and a hat that we didn’t give her… there’s nothing of [my partner] and I in that picture - that’s not even one of our hands… she’s now separate from me, she’s just been taken out of my tummy and taken away from me - that’s what that picture means to me .Once they are in the cot then the next step is home, so it felt like we took a real leap that day ‘cause she didn’t have her monitors. […] I could lift her out of the cot, hold her, nurse her… it just felt so much more natural’ (Mia)During this shift from not feeling like a parent to regaining one’s parent identity, participants also reported seeking ways to establish an emotional connection and to make up for lost time.I just thought ‘I didn’t get to hold this baby for six weeks so I’m going to make up for all these cuddles because before you know it, she’ll be walking, and I’ll not be able to hold her all the time’ (Kate)The concept of returning to normality appeared across all transcripts. This involved reaching milestones such as leaving hospital, but also participants asserting their needs to establish a normative bonding experience to regain their parenting role.This is just me and my baby and I’m just going to sit here and hold him and that’s okay […] it was the realisation of that, and I suppose just really trying to get to know him, getting our independence and not obsessing about being in a routine (Maria)In this subtheme, participants reflected on how they adjusted to parenting a premature infant and spoke of how their experience was different from parents of full-term infants. They described themselves as engaging more in overcautious behaviors to protect their infants from illness and relapse.[Other mummies] are not as obsessed with sterilising and cleaning. I suppose I had been in that sort of institutionalised environment - I was so into sterilising, infection control, all the things I suppose other preemie mummies were (Kate)Participants described the impact the NICU environment and hospital protocols had on their parenting. Their parenting experiences upon birth began with an initial dependence on the medical equipment to keep their infants alive within hospital. Participants also spoke of the hospital medical routine and protocols dominating their home life; “there were a lot more appointments, there was a lot more check-ups, and she came home on medication… so there was a lot more that I had to do” (Evelyn).There are lots of machines and lots of beeping and lots of things going on, but it’s so quiet and it’s almost like there’s a fear you know even though all the parents are there and it’s all their children, but there seems to be almost this fear and you just sit and you stare and it’s very strange (Jane)In NICU, there was an underlying fear of infants becoming progressively ill or dying. Therefore, participants rejoiced when their infants reached milestones, as it represented progression and hope. These instances often began with participants realizing that their infant was born alive and being thankful for this. Paying attention to milestones reached within the NICU, helped participants feel closer to being discharged and embracing normality in their parenting. These milestones tended to be related to specific aspects of the infants’ medical care and physical well-being.There was so many small steps but they were happening every hour every day, like his feeds, if a feed increased, that was amazing, you were focused on all the tiny milestones, like his weight going up by a couple of grams, so I just took it day by day (Lisa)We used this [photograph] as a progress picture…one of the things I liked was when it was in the cot beside him - it was nearly the same size as him [laughs] and now he has pictures of it nearly every month with it sitting beside him. You can just see how much bigger he is and healthier Participants depicted their infants as having strength which gave them comfort that they would continue to thrive and survive; “I just think of her journey and what she’s came through and I’m like ‘how amazing’ as an adult we […] fall sick we just lie there, whereas these babies fight, and these babies are just incredible” (Helen).Within this subtheme, participants described natural processes where they accepted the situation over time, learnt to deal with their emotions, and reinterpreted their experience; “I just got to a point I was like ‘d’you know what, I couldn’t have done anything different, like he’s here now and he’s healthy just it’s not my fault’” (Lisa).Even a year ago if you had of said ‘tell me your story’ I would have broken down in tears halfway through and couldn’t have told you anymore, but I think I have dealt with much more and I have battled through it, and I have lived it […]it’s a raw emotion still but I’ve learnt how to manage those emotions you know? I don’t think you would ever get over it in the sense that it is just something that happened (Helen)Examples of actions that helped participants, or advice they would suggest to other parents were also discussed as part of the learning and growth process. These included accepting the help of others, having more confidence in medical staff expertise, taking each day as it comes, not engaging in thoughts of self-blame, and keeping self-informed about medical procedures; “take the help that you can get, listen to people, and never ever blame yourself, ‘cause that’s probably what ate me for so long’” (Mia). Participants also recognized service improvement areas. One participant spoke of wanting more support for parents when dealing with the experience of leaving their infants each night.You are never gonna feel fine leaving them, but I think just if somebody sort of came to you and said you know ‘this is normal to be feeling like this’ you know ‘try doing this’ or even just get you prepared for that (Jill).Interpersonal support reflects the impact that other people had on helping participants to cope and overcome adversity. Participants described specific interactions with staff members which helped them while in hospital such as reassurance provided, explaining medical terminology, prioritizing their emotional well-being, and responding to their concerns. Participants also described specific interactions with those outside of the immediate medical care , which helped them cope better and move beyond adversity: “having somebody to talk to who’s also had a premature baby, on a day-to-day basis about various things has been really really lovely, so I value that because I think I would have really struggled had I not had that” (Maria).Overall, when recounting their experiences of having an infant born premature, participants described moving from a state of emotional distress to a state of growth and acceptance. All participants in the study reported initially feeling distressed by the experience and the hospital environment. As depicted in existing findings, participants described feeling fearful, shocked, experiencing post-traumatic stress symptoms, and that they were to blame . SimilarParticipants also described moving from thoughts and emotions of not feeling like a parent to adjusting to a different kind of parenting. The difference was in relation to comparisons made with parents of full-term infants and prior expectations about parenting. Parents of premature infants described themselves as initially being preoccupied with medical routines and appointments, having clean home environments, and not wanting to expose their infants to outside settings. Over time, they described embracing the difference and returning to normality. This process seemed to occur as their infant became older, less dependent on the medical system, and as parents became less fearful of illness. These findings are consistent with the literature on parents’ regaining confidence following discharge. Interview guides and the order of questions can influence responses and subsequent analysis within qualitative research. Results showed a sense of a progression or journey that participants went through. This may have reflected the nature of the interview questions, which began with the birth story and ended with questions on current parenting experiences. This potential limitation was addressed through asking open-ended questions during the interview. Additionally, the research did not capture the experience from both parental perspectives and data were not collected on participants' racial and ethnic representation, potentially leading to a misrepresentation of the diverse kinds of parents living in Northern Ireland who experience a premature birth. All parents that participated were first time mothers and therefore the research did not capture being an experienced parent with a premature infant. These limitations may have been due to recruitment through social media and the kind of parent that engages with these mediums for information . Notwithstanding these limitations, the sample was more homogenous than other studies in this area. Qualitative studies within this population have tended not to be homogenous and have comprised samples where individuals vary greatly on specific factors, including infants from wide ranging gestational ages and parents being interviewed at different timeframes since their premature birth experience . AlthougAcross the interviews, participants described emotional difficulties they experienced regarding their parent identity and infant’s prematurity and provided ideas on how their transition to parenthood in the NICU and beyond could be better supported. Along with existing studies highlighting emotional distress within similar samples, this supports the need for psychological interventions to be imbedded in hospital settings. Parent support interventions in the NICU could be delivered in the form of psycho-educational programs and psychological therapy, which have shown their effectiveness in relieving psychological distress in this population . ParticiThe present study revealed that participants did not feel like parents initially and wanted to be more involved in the care of their infants. Parent-partnered care models such as Family Integrated Care aim to train parents to be more involved in their infants’ care through being part of the NICU care team . This moNone to disclose.


Fourier-transform infrared spectroscopy gave peaks at 1726 cm−1 (C=O) and 1573 cm−1 (RCOO−), indicating the formation of OS-TBS. We further studied the physicochemical properties of the modified starch as well as its emulsification capacity. As the DS with octenyl succinate anhydride increased, the amylose content and gelatinization temperature of the OS-TBS decreased, while its solubility increased. In contrast to the original Tartary buckwheat starch, OS-TBS showed higher surface hydrophobicity, and its particles were more uniform in size and its emulsification stability was better. Higher DS with octenyl succinate led to better emulsification. OS-TBS efficiently stabilized O/W Pickering nanoemulsions and the average particle size of the emulsion was maintained at 300–400 nm for nanodroplets. Taken together, these results suggest that OS-TBS might serve as an excellent stabilizer for nanoscale Pickering emulsions. This study may suggest and expand the use of Tartary buckwheat starch in nanoscale Pickering emulsions in various industrial processes.In this study, Tartary buckwheat starch was modified to different degrees of substitution (DS) with octenyl succinate anhydride (OS-TBS) in order to explore its potential for stabilizing Pickering nanoemulsions. OS-TBS was prepared by reacting Tartary buckwheat starch with 3, 5 or 7% ( Fagopyrum tataricum, (L.) Gaertn.) is a traditional edible and medicinal pseudo-cereal enriched with beneficial phytochemicals, including flavonoids, phenolics, steroids, fagopyrins, and d-chiro-inositol [Tartary buckwheat  [–14 μm 3. HoweverOctenyl succinic anhydride (OSA)-modified starches, approved for food use by the US Food and Drug Administration in 1972, have been prepared from oat, quinoa, sago, maize, and wheat ,11 and wThe improvement of the surface properties of the starch granules and the choice of the appropriate physical treatment technique are essential for the formation of Pickering nanoemulsions. High-pressure homogenization (HPH) is a non-thermal physical process for reducing the particle size of a sample (emulsion or suspension) from the micron range to the nanometer range by using shear, impact and cavitation effects at high pressure . Due to Therefore, we investigated the potential of OSA-modified TBS  as a stabilizer for Pickering nanoemulsions. In particular, we focused on the effect of the modification on the physicochemical properties of the starch granules and on the formulation and process of nanoemulsion preparation. The size, chain length, viscosities and thermal characteristics of starch influence their emulsifying characteristics ,27. ThusTartary buckwheat was obtained from the Key Laboratory of Coarse Cereal Processing at the Ministry of Agriculture and Rural Affairs at Chengdu University ; 2-octenyl succinate anhydride , from Shanghai Maclean Biochemical ; as well as amylose standard (96% pure) and amylopectin standard (87.2% pure), from Beijing Northern Weiye Institute of Metrology and Technology ; medium-chain triglycerides from Shanghai Yuanye Bio-Technology . All other chemicals in this study were analytical grade and purchased from Chengdu Kelong Chemical Company .w/v) for 30 min at 50 °C and 500 W in order to remove flavonoids and lipids. The precipitate was soaked for 24 h at room temperature in 0.3% NaOH solution at a ratio of 1:10 (w/v), then passed through gauze in order to further remove crude fibers and other impurities. The resulting starch slurry was centrifuged for 10 min at 4000 rpm; the supernatant and upper brown layer were discarded, and the remaining white layer was washed again with 0.3% NaOH. Centrifugation and washing with 0.3% NaOH were repeated three times. The final precipitate was dispersed in distilled water, neutralized to pH 7.0 by addition of 0.1 M HCl, washed with distilled water and centrifuged repeatedly until the supernatant was clear without brown layers and it formed a firm, stable white precipitate at the bottom of the tube. Finally, the precipitate was dried at 40 °C for 48 h, ground into a powder and passed through a 100-mesh sieve to eliminate agglomeration. The resulting starch was stored in a polyethylene bag at room temperature for later use.TBS was isolated as described  with sliw/v) was dispersed in distilled water with continuous stirring. OSA was diluted with anhydrous ethanol and dropped slowly into the TBS dispersion within 2 h while temperature was maintained at 35 °C. The pH of the starch slurry was adjusted to 8.5 with 3% NaOH solution and the esterification reaction was allowed to continue for 3 h at 35 °C. Then the pH of the starch slurry was adjusted to 7 using 1 M HCl. The slurries were centrifuged for 15 min at 4000× g, washed several times with distilled water, washed twice with 90% ethyl alcohol, dried at 45 °C for 24 h, passed through a 100-mesh sieve and stored in polyethylene bags at room temperature.OS-TBS was prepared using a method based on a previous report ,29. TBS The different amounts of OSA  were added to the TBS dispersion, and resulting slurries were denoted by OS-TBS-3, -5, -7, respectively.v/v) isopropanol was added to each sample, stirring was continued for 10 min, and the mixture was centrifuged at 3000× g for 10 min. The sediment was washed thoroughly with 90% isopropanol until addition 0.1 M AgNO3 did not lead to appreciable formation of AgCl. The washed sediment was suspended in 30 mL of distilled water, heated in a boiling water bath for 30 min, and titrated with 0.1 M NaOH solution using phenolphthalein as the end-point indicator. DS was calculated using the formula [A was the titration volume of NaOH solution (mL), M was the molarity of NaOH solution, and W is the dry weight (g) of the OS-TBS. Native TBS served as the reference.DS of OS-TBS samples was determined using a titration-based method . Briefly formula :(1)DS=16The amylose content of TBS and OS-TBS was determined using an iodine-binding method . Starch w/w) and spectra were obtained from 400 to 4000 cm−1 at a resolution of 4 cm−1 [The chemical structure of TBS and OS-TBS was analyzed qualitatively using a Spectrum Two FT-IR spectrometer . Samples were prepared by grinding the finely powdered starch with KBr . Starch samples were suspended in distilled water over a range of light obscurations from 10 to 20%. Volume-averaged droplet size (D) was determined as described ,4 by assGranules were observed under a scanning electron microscope . Samples were mounted on double-sided adhesive tape on an aluminum stub, a layer of gold was sputtered on top, and the samples were imaged at an accelerating voltage of 15 kV. Images were taken at different magnifications (×2000 and ×5000) at a working distance (WD) of 14 mm to observe the dense structure of the particles and pores .θ) from 5 to 40° at a rotational speed of 6.35 °/min [The crystalline structure of TBS and OS-TBS was examined using an X-ray diffractometer  operating at 40 kV and 40 mA with Cu-Kα radiation. Diffractograms were obtained over a range of diffraction angles (2 35 °/min . Relativ35 °/min  using MDTo), peak temperature (Tp), conclusion temperature (Tc) and gelatinization enthalpy (ΔH) were measured as described [The onset temperature (escribed  using a escribed .w/v) in deionized water and incubated for 30 min in a water bath at 50, 60, 70, 80 or 90 °C, then centrifuged at 4000 rpm for 15 min. The supernatant was recovered and dried to a constant weight at 105 °C. The WSI and SP were calculated using the following equations:W0 was the weight of starch; sW, the weight of the sediment after centrifugation; and W1, the weight of the supernatant after drying.The water solubility index (WSI) and swelling power (SP) of starch samples were determined as described . The TBSStarch powder was compacted into a standard tablet 2 mm thick and the tablet was then immersed in medium-chain triglycerides. Then, 16 μL of deionized water was dripped lightly onto the surface of the tablet for 1 min and allowed to equilibrate. Three-phase contact angles were determined using a JY-82B device  based on taking photos and the protractor method .The preparation of Pickering emulsion samples were determined as described ,41 with v/v) in deionized water, and one drop of emulsion was poured onto the glass microscope slide. The distribution of drop sizes was also measured using a Zetasizer Nano 90 system , based on refractive indices of 1.414 for medium-chain triglycerides and 1.33 for water [The distribution of drop size in Pickering emulsions was observed using a TL3900CA optical microscope  at an image magnification of 10 × 100. The emulsions were diluted 1:5  was transferred to a 15 mL sample vial, sealed and stored at room temperature and photographed at 0, 15 and 30 days. The emulsification index (EI) of the corresponding Pickering emulsions were evaluated by the volume/height of the emulsified and precipitated layers after 1d and the EI was calculated as described  using thg for 10 min as described [The stability of emulsions with centrifugation was investigated after centrifugation at 10,000× escribed  using a p < 0.05. Data plots were prepared using Origin 2021 software .All experiments were performed in triplicate, and data were reported as mean ± standard deviation. Data were analyzed statistically using one-way analysis of variance (ANOVA) in SPSS 25.0 . Differences were considered significant if Increasing the amount of OSA from 3 to 7% during the preparation of OSA-modified Tartary buckwheat starch increased the DS from 0.0184 to 0.0312 , reflect1, tensile vibrations of C-H and bending vibrations of absorbed water at 2930–1640 cm−1 [−1 [−1, due to the C-O telescopic vibration of the ester carbonyl; and one at 1573 cm−1, due to asymmetric stretching of the RCOO vibration. These peaks were interpreted to indicate successful formation of OS-TBS because they were similar to previous reports [FT-IR analysis of TBS revealed strong O-H stretching vibration peaks at 3800–3000 cm640 cm−1 , and strcm−1 [−1   than for the unmodified TBS (18.8 μm), B. Additiθ = 15, 17, 18 and 23° [XRD analysis revealed TBS to have an A-type X-ray diffraction pattern with intense peaks at 2 and 23° . The cry and 23° ,42,49.oT, pT, cT and ΔH  of OSA. The OSA modification slightly increased the particle size distribution of TBS and reduced the apparent straight-chain starch content. XRD patterns showed that the OSA modification occurred mainly in the amorphous region and had less effect on the crystalline region, which is consistent with the OSA treatment maintaining the morphological results of the starch, which are consistent with the OSA treatment maintaining the shape and integrity of the granules. We also investigated the effect of various factors on the emulsification properties of Pickering emulsions prepared by the HPH method using OS-TBS as a stabilizer. Our results suggest that Pickering nanoemulsions can be maximally stabilized by OS-TBS when the HPH method is used with a stabilizer concentration of 4 wt%, an oil phase volume fraction of 30 vol%, minimal ionic strength and neutral pH. The higher DS in OS-TBS result in smaller and more stable emulsified particles. Although the effects of various factors  on the physicochemical properties of OSA starch have been extensively investigated, this study further demonstrates the synergistic effects of OSA treatment, small particle-size characteristics and HPH methods in the preparation of Pickering nanoemulsions. Our work provides the first demonstration that a combined strategy offers the unique advantage of significantly improving the properties of TBS and lead to stable Pickering nanoemulsions for various industrial applications.


Objective: To quantify the effects of increasing the step length of the split squat on changes in kinematics, kinetics, and muscle activation of the lower extremity.Methods: Twenty male college students participated in the test . Data on kinematics, kinetics, and EMG were collected during split squat exercise at four different step lengths in a non-systematic manner. One-way repeated measurements ANOVA were used to compare characteristic variables of peak angle, moment, and RMS among the four step length conditions.Results: The step length significantly changes the peak angles of the hip (p = 0.011), knee (p = 0.001), ankle (p < 0.001) joint, and the peak extension moment of the hip (p < 0.001), knee (p = 0.002) joint, but does not affect the ankle peak extension moment (p = 0.357) during a split squat. Moreover, a significant difference was observed in the EMG of gluteus maximus (p < 0.001), vastus medialis (p = 0.013), vastus lateralis (p = 0.020), biceps femoris (p = 0.003), Semitendinosus (p < 0.001), medialis gastrocnemius (p = 0.035) and lateralis gastrocnemius (p = 0.005) during four step lengths, but no difference in rectus femoris (p = 0.16).Conclusion: Increases in step length of split squat had a greater activation on the hip extensor muscles while having a limited impact on the knee extensor muscles. The ROM, joint moment, and muscle activation of the lead limb in the split squat all should be considered in cases of individual preventative or rehabilitative prescription of the exercise. Moreover, the optimal step length for strength training in healthy adults appears to be more suitable when it is equal to the length of the individual lower extremity. The optimal exercise selection for improving, maintaining, and enhancing functional capacities involves aligning the demands of an exercise with the specific needs of the client or patient . Using aA split squat, or a forward lunge, is a multijointed exercise of a closed kinetic chain used to improve the function or strength of the lower extremities . The maiAccording to a general statistic, joint injuries are very common in the athletic population, with an incidence of 10–35.5 injuries per 1,000 . Most ofRecently, it was shown that the front tibia angle influences joint angles and loading conditions during the split squat exercise . SuggestNo studies have been conducted to observe the kinematics and kinetics of the lower extremity among multiple different step lengths creating a void in the existing literature.The strength of the lower extremity muscles, particularly those around the knee, and the ratios between different muscle strengths, play a crucial role in rehabilitation and strength training. Some studies have pointed out through EMG that the vastus medialis (VM) and vastus lateralis (VL) are two of the key muscles that control the frontal plane kinematics of the knee , which mThe activation of specific muscles can be varied by performing variations on the same exercise. To our knowledge, performing variations of split squats or lunges may alter the muscle activation of the lower extremities, possibly resulting in changes in strength that may be important both for the rehabilitation of patients and the strength training of healthy adults. For example, certain types of squats and lunges result in different activation of the VM compared to the VL . SpecifiIn summary, the objective of this study was to describe the kinematics, kinetics, and muscle activation of the lower extremities during split squat among four different step lengths. It was hypothesized that the peak angle and the extension moment of the hip increases with the increase of step length, and decrease for the knee. The EMG activity of the hip extensor increases with the increase of step length, and decrease for the knee extensors. The results of this study may provide theoretical support for guiding the selection of exercises or training programs, both for patients’ rehabilitation and healthy adults’ strength training.Twenty male college students majoring in physical education who engage in regular exercise (at least twice a week) participated in this study . Participants who had previous neurological disease, hypertension, or orthopedic pathology were not included in this study. The mean height and mass of the participants were 1.75 ± 6.4 m and 81.2 ± 3.8 kg, respectively. The mean 1RM of the split squat was 1.1 ± 0.3 kg/BW. All participants were familiar with the split squat. The protocol for this study was approved by the Health Sciences Institutional Review Board (NO.2020187H), and participants provided their informed written consent prior to participation.Thirteen retroreflective markers, each 14 mm in diameter, were affixed to palpable body landmarks to estimate the rotational centers of the ankle, knee, and hip. Markers were placed bilaterally at the anterior superior iliac spine (ASIS), the top of the crista iliaca, the L4-L5 interface, the anterior thigh, the lateral and medial femur condyles, the lateral and medial malleolus, the tibial tuberosity, the center of the second and third metatarsals, and the heel of dominant limb. Three-dimensional kinematic data were collected using an 8-camera motion analysis system at 200 Hz . Kinetic data were collected using force plates , which were embedded in the floor and sampled at 1000 Hz. The coordinate and ground reaction force signals were time-synchronized using Cortex, version 2.6.2 .Surface electromyography (EMG) of eight muscles, was recorded using silver-contact wireless bipolar bar electrodes with fixed 1 cm interelectrode spacing . Electrodes were placed parallel to muscle fibers of gluteus maximus (GM), vastus lateralis (VL), vastus medialis (VM), rectus femoris (RF), biceps femoris (BF), Semitendinosus (ST), medialis gastrocnemius (MG), lateralis gastrocnemius (LG). A maximal voluntary isometric contraction (MVIC) was performed for each of the eight muscles to elicit maximal activity, as previously described in the literature , and to At the first visit, the 10RM barbell weight for each subject’s split squat was determined by a standardized protocol. After a standardized warm-up (jogging and dynamic stretching), an estimated 10RM weight was selected for the split squat. When the maximum number of reps was greater than 10, the weight was increased until the maximum number of squats was 10. Oppositely, when the maximum number of reps is less than 10, decrease the weight until the maximum number of squats is 10. Each increase or decrease in weight is 10% of the estimated 10RM weight. During all the attempts, each participant was asked to squat until reaching a depth where the thighs were parallel to the floor. In addition, each subject’s 10RM barbell weights were determined using a comfortable step length.During the second visit, all subjects were required to complete split squats under four step length conditions  while kinematic, ground reaction force, and EMG data were collected. During each condition, subjects were required to perform split squat exercises for three consecutive repetitions, using their 10-repetition maximum (10-RM) barbell weight. To control for multiple exposure and fatigue effects, each participant was randomly assigned a step length condition order. The data was collected mainly from the dominant lower extremity, which was positioned in front, while the non-dominant side was positioned behind. The dominant lower extremity was operationally defined as the preferred limb for kicking a ball. A 72-h resting period was given to participants between the 10RM procedure and the formal data collection .Each subject participated in the preliminary experiment 72 h before the formal experiment. For the split squat exercise, all participants wore the same brand  and style of shoes. Four step lengths were determined for each subject based on their own leg length, and each subject was given the opportunity to practice the split squat for the four conditions. The stepping length for the split squat was standardized by using the leg length, which was measured from the greater trochanter to the lateral malleolus, and four different lengths were set at 50%, 70%, 100%, and 120% of leg length. Tape strips were placed on the floor at the starting point and target step length .First of all, participants were instructed to step forward into a split stance with the dominant limb on the force plate and then complete each repetition by lowering the body until the front thigh was parallel to the floor. Once they reached the lowest position, they were instructed to immediately rise upward and return to the split standing starting position. They were also instructed to maintain an erect torso during the entire split squat. A video camera recorded a view of the sagittal plane from the right side, which was chosen to make sure the erect torso and thighs are parallel to the floor. Participants attempted to complete the lowering phase of each repetition within 2 s; an acoustic metronome set to 60 beats per minute was used to assist with movement. Several familiarization trials  were allowed for each step length condition before data collection so that the participants could become comfortable with the movements and length.Lower extremity kinematics, kinetics, and EMG data were collected simultaneously in all four step length conditions. The original 3-dimensional coordinate data of the markers were filtered using a Butterworth low-pass digital filter with an estimated optimal cutoff frequency of 13 Hz . Visual p < 0.05. When statistical significance was evident for the four split squat characteristics, LSD post hoc tests were used. All statistical tests were conducted in Statistical Package for Social Science software .For each dependent variable, the average across the three trials within each of the four step length conditions was calculated and used for statistical analysis. One-way repeated measures analyses of variance (ANOVAs) were used to compare the split squat characteristic variables  among the four step length conditions. In all analyses, a Greenhouse-Geisser correction factor was applied when sphericity was indicated. The statistical significance was set at p = 0.011), knee joint (p < 0.001), and ankle joint (p < 0.001) among different step lengths. Post hoc analysis revealed that the peak hip flexion angle at 100% LL step length was significantly smaller than that at 70% LL step length (p = 0.005), and 120% LL step length (p = 0.019). The flexion angle at 50% LL step length tended to be greater than that at 100% LL step length (p = 0.064). The peak knee flexion angle at 100% LL step length was significantly smaller than that at 50% LL (p = 0.002) and 70% LL (p < 0.001) step length. The angle at 120% LL step length was significantly smaller than the angle at 50% LL (p < 0.001) and 70% LL (p = 0.001) step length. The peak ankle flexion angle at 120% LL step length was significantly greater than the angle at 50% LL (p < 0.001), 70% LL (p < 0.001), and 100% LL (p < 0.001) step length. The peak ankle flexion angle at 100% LL step length was significantly greater than 50% LL (p < 0.001), and 70% (p < 0.001) step length. The peak ankle flexion angle at 70% LL step length was significantly greater than the angle at 50% LL (p = 0.048) step length.Descriptive statistics for the peak flexion angles are presented in p < 0.001) and knee joints (p = 0.002) among different step lengths, and no significant differences in ankle peak extension moment (p = 0.357). Post hoc analysis revealed that the peak extension moment of the hip joint at 120% LL step length was significantly greater than that at 100% LL (p = 0.013), 70% LL (p = 0.008), and 50% LL (p = 0.008) step length. The peak hip extension moment at 50% LL step length was significantly smaller than that at 70% LL (p = 0.008) and 100% LL (p < 0.013) step length.Descriptive statistics for the peak net joint moment are presented in p < 0.001), the VM (p = 0.013), the VL (p = 0.020), the BF (p = 0.003), the ST (p < 0.001), the MG (p = 0.035) and the LG (p = 0.005) during the lunge for all step length, and no significant difference in RF (p = 0.16). In the post hoc test. The root mean square amplitude (RMS) for GM at 50% LL step length was significantly smaller than the RMS at 70% LL (p = 0.020), 100% LL (p < 0.001), and 120% LL (p < 0.001) step length, and 70% LL step length was significantly smaller than the RMS at 120% LL step length (p = 0.037). The RMS for VM at 50% LL step length was significantly smaller than the RMS at 100% LL (p = 0.010) and 120% LL (p < 0.012) step length. The RMS for VL at 50% LL step length was significantly smaller than the RMS at 70% LL (p = 0.006) step length, and 120% LL step length was significantly smaller than the RMS at 100% LL step length (p < 0.001). The RMS for BF at 50% LL step length was significantly smaller than the RMS at 70% LL (p = 0.038), 100% LL (p < 0.001), and 120% LL (p < 0.001) step length, and 70% LL step length was significantly smaller than the RMS at 120% LL step length (p < 0.001). The RMS for ST at 50% LL step length was significantly smaller than the RMS at 70% LL (p = 0.045), 100% LL (p = 0.002), and 120% LL (p < 0.001) step length, and 70% LL, 100% LL step length was significantly smaller than the RMS at 120% LL step length (p < 0.001). The root mean square amplitude (RMS) for MG at 50% LL and 70% LL step length was significantly smaller than the RMS at 100% LL  and 120% LL  step length, respectively. The RMS for LG at 50% LL and 70% LL step length was significantly smaller than the RMS at 100% LL  and 120% LL  step length, respectively.Descriptive statistics for muscle activation are presented in The purpose of this study was to compare the differences in kinematic, kinetic, and muscle activation characteristics of the anterior lower extremity when performing a split squat with leg length-based standardized step lengths in healthy adults. There is a significant change in the peak flexion angle of the hip joint. The range of motion (ROM) decreased, while the peak extension moment increased with the increase in step length. Peak knee flexion angle, knee extension moment, and peak ankle angle decrease with increasing step length. In general, a strength exercise is executed safely within the physiological ROM of a joint and by avoiding overloading of the human tissue . Our resIn order to avoid the effects of trunk angle and squat depth on the biomechanical characteristics of the lower extremity among four step lengths, we asked all subjects to keep the torso upright and squat down to the position where the front thigh was parallel to the ground at four split lengths. Therefore, in theory, step length will not affect the peak flexion angle of the hip joint, but the experimental results are inconsistent with our hypothesis. The peak flexion angle of the hip at 100% LL step length was significantly smaller than at 70% and 120% LL step length . The reaAs shown in our data, the ROM of the hip joint gradually decreases and the peak net moment gradually increases with increasing step length, which is more challenging for the hip extensors. Our results supported the work of As S. P. The split squat is a movement dominated by the front lower extremity . TheoretThe peak knee extension moment showed a gradual overall decrease with increasing step length but was significantly greater at 70% LL step length than at 50%. The reason could be the over-short step length (50% LL) during split squatting causing a backward shift in the body’s center of gravity, leading to increased load on the posterior limb. But our experiments did not collect data from the posterior limb, which is a limitation of our study. However, some studies  showed tMany previous studies have shown that excessive peak extension moment in sports may cause cartilage or ligament tissue damage , and PatMany adults and even athletes will have varying degrees of dorsiflexion restriction, which is an important factor in athletic performance or injury . The maxAt 120% LL step length, the split squat is entirely in the plantar flexion of the foot, which means that the entire process of squatting works out the calves within the vertical line. Therefore, we recommend that for some patients with limited dorsiflexion of the foot, split squat training be performed with ≥100% LL of the step length. Despite greater variation in peak ankle dorsiflexion angles at different step lengths, there was no statistically significant difference in peak ankle extension moment.Our results seem to indicate that increasing step length can lead to more favorable kinematics and dynamics of the lower extremity joints. However, during the practice process, it became evident that excessively large step lengths  negatively impact movement maneuverability, often resulting in forward slipping. Notably, significant improvements were not observed beyond a step length of 100%LL.The kinematic and kinetic data for a given movement reflect the outward appearance of all muscles involved in the movement, EMG provide a good indication of the activation of a muscle during a particular phase of a given movement. The results of the EMG data in this study can reflect the activation of the relevant muscles during the split squat movement at four step lengths . The splThere are many clinicians and health professionals who recommend their patients strengthen the quadriceps with split squat or lunge movements. Therefore, appropriate training movements are important to optimize muscle activation. Same as our hypothesis, the activation of the hip extensors, knee extensors, and flexors was significantly less at 50% LL step length than the other three lengths. We can speculate that this may be due to the small step length of the split squat, thus increasing the load on the posterior lower extremity and reducing the activation of the anterior lower extremity muscles. Hofmann’s study also demonstrated that reducing step length resulted in more weight-bearing on the posterior limb .Anatomically, the RF serves the dual function of flexing the hip and extending the knee, while the BF and ST perform the opposite functions. Therefore, it can be challenging to determine whether the RF and BF/ST is engaging in a centrifugal or centripetal contraction during a split squat . HoweverConsistent with the previous results , the EMGThe EMG of MG and LG was only significantly different between the longer (100% LL and 120%LL) and shorter (50% LL and 70% LL) step length. We hypothesized that a 10RM load would result in a higher demand for ankle stability when the step lengths are larger. These all results may provide a clearer training protocol for patients rehabilitating from clinical muscle atrophy but also requires consideration of whether the patient’s knee joint mobility and maximum net joint moment are within the patient’s tolerance range.With the step length variations, the ROM of the hip, knee, and ankle joints, peak joint net moments, and the EMG of lower extremities underwent changes. Specifically, with the step length increases, the ROM in the knee and ankle joints tends to decrease, while the peak extension moment of the hip joint increases. In addition, alterations in step length had a greater influence on the hip extensor muscles, while having limited impact on the knee extensor muscles, especially between 100% LL and 120%LL. But in practice, if the length of the step goes beyond 120% LL of the step length, it would require more stability and induce only minor enhancements in muscle activation. It is recommended that the appropriate step length should be selected based on specific needs, such as considering joint restrictions, avoiding excessive joint stress, or increasing specific muscle activation when customizing rehabilitative exercise prescriptions. The optimal step length for strength training in healthy adults appears to be more suitable when it is equal to the length of the individual lower extremity.


R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas.Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the  Surface subsidence is a common geological environmental disaster, which is increasingly concerned –4. It isThe existing models for predicting surface subsidence are mainly divided into two types: physical models and statistical models –14. PhysThe emergence of Graph Convolutional Network (GCN) –26 is wen nodes of the graph structure, and the information of each node is uniquely represented by an m-dimensional vector, and the set of edges n dimensional symmetric 0 − 1 matrix A, which is usually referred to as the adjacency matrix. The adjacency matrix A is generated by the following equation.In general, the graph structure data consists of a set i − th node in the l − th layer.GCN can aggregate local information between neighboring nodes by performing convolution operations on the graph. When performing multi-layer convolution operations, the information in the graph can be passed farther through the connections between nodes, achieving the purpose of fusing local and global information. The following equation updates the hidden state Wl is the weight matrix, j − th node in the l − 1th layer, bl is the bias, and σ is the nonlinear activation function . When l = 1, the hidden feature In the Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for establishing a prediction model with MRF-GCN. The main contributions of this paper are as follows.We design and implement a prediction model MRF-GCN for surface subsidence based on graph convolutional networks. The model runs a graphical convolution operation in the surface subsidence monitoring network, which can model the interaction between monitoring points due to geological heterogeneity and thus make more accurate predictions of subsidence at surface points.We successfully predicted the surface subsidence pattern in a mining area in China by the model proposed in this paper and elaborated on the principles of the model work. Meanwhile, we designed ablation experiments to confirm the effectiveness of the model modules.https://github.com/BaoSir529/MRF-GCNWe write the code based on the Python language and implement it based on the latest Pytorch deep learning architecture. To facilitate related research, our code is publicly available at As shown in P = {p0, p1, …, pn}. For a given surface monitoring point, there are continuous time series data ti = {t0, t1, …, tn}. Depending on the type of monitoring, these time-series data can provide feedback on changes in data at the site over time . The surface subsidence prediction task aims to predict data for a future period based on a priori data ti, i.e., to predict {tn+1, tn+2, …., tn+k}.In the case of large-scale surface subsidence monitoring, denote a series of surface monitoring points as In order to obtain subsidence data for a particular monitoring area, a monitoring network needs to be set up several years in advance, and the observation area needs to be continuously observed for an extended period by manual level measurement or in combination with remote sensing technology. In this paper, we selected the surface of the 82/83 mining area of Yuandian No. 2 Mine in Anhui Province, China, as the observation area. We set up the monitoring network in advance and obtained the continuous deformation data in this area by manual level measurement and InSAR image-based extraction. In order to confirm the reliability of the model, we select two observation networks with 20 monitoring points in the manual measurement area and the InSAR image processing area, respectively, as the demonstration objects of this paper.According to China National General Specification for Engineering Surveying requirements, we select the monitoring points within the artificial measurement area. In addition, when selecting points, avoid extremely sloping ground and water and choose sites that can be preserved for a long time and are easy to observe. The relationship between monitoring points in all observation networks follows the following guidelines.More excellent connectivity between monitoring sites in close spatial proximity.More excellent connectivity of monitoring points on the same geological structure.Lack of connectivity between monitoring sites isolated by large geological structures  or building complexes.The connection between monitoring points should consider the actual placement of the level observation network.A, and the subsequent contents do not distinguish between the two regions.The control network is laid in the remote sensing area, and the manual measurement area is shown in P = {p0, p1,., pn}, each monitoring point pi obtains a time series information reflecting its elevation change After continuously observing monitoring points y (LSTM) , 29 netwy (LSTM) , which ck steps of the preliminary data and train the model to learn the patterns of all sequential data by scanning the prior data item by item with the sliding of this window. The specific experimental procedure of this process is that, first, the sequence data of each monitoring point in the control network is divided into overlapping data segments according to the window width K. For a monitoring point pi the time series information is divided into n − k + 1 data segments, notated as l-layer LSTM module, with each segment of length k as an input state at the current time t. For each monitoring point input, the following function is computed for each layer of the LSTM module.ht is the hidden state at time t, ht−1 is the hidden state of the layer at time t − 1 or the initial hidden state at time 0, Ct is the cell state at time t, xt is the input at time t, and It, Ft, Gt, Ot are the input, forget, cell, and output gates, respectively. ⊙ is the Hadamard product. Wi and bi is the corresponding parameter matrix and bias. In a multilayer LSTM, the input l − th layer is the hidden state l layers of the LSTM module, for all monitoring points in the detection network, the hidden state Hlstm.To achieve the goal of learning future subsidence patterns using a priori sequences, we set up a window (window) that allows us to look forward at Finally, to facilitate the subsequent graph convolution operation, the subsidence features extracted by LSTM are subjected to nonlinear changes to extract the association information between them and finally mapped to a new feature space. Their spatial dimension is set as the input dimension of the graph convolution module.k consecutive a priori monitoring data, where c is the input dimension of the graph convolution module, W and b are the parameter matrix and bias of the nonlinear transformation.The final coded information The observation area of the continuous surface subsidence monitoring task is large, and the geographical factors are complex. The points in the monitoring network interact with each other and have complex intrinsic interactions. For example, changing points in the monitoring network within the area to be trapped can continuously affect surrounding points. In order to better tap the interaction pattern between points, we introduce a designed graph convolution module. By modeling based on the graph structure information of the monitoring network and the information of the continuous settlement pattern of the monitoring points, it helps the model to make a more optimistic prediction of the surface settlement.k a priori data. In the actual monitoring, the monitoring data of each monitoring point in the monitoring network are obtained in the same period and independently measured. This pattern is often reflected in the monitoring data of the duration when the monitoring points in the same monitoring network interact with each other. According to the graph generation module and LSTM coding module mentioned above, we obtain the adjacency matrix A of points within the monitoring network and the continuous variation law H of each monitoring point fed into a designed graph convolutional neural network.After the LSTM coding module, the model obtains the coding information of each monitoring point in the monitoring network about the continuous I is a unit matrix, and D is the degree matrix of A, storing the number of nodes that each node joins to the rest.To eliminate the effect of non-normalization of the monitoring point relationship map, we normalize the point adjacency matrix according to the suggestion of Kipf & Welling .e=D-12Al by aggregating the state information of neighboring monitoring points in layer l − 1, and the network is operated according to the following function.pi at the l-th layer of GCN. pj’s neighbor node pi’s output at the l − 1-th layer of GCN, and also as the input of the l-th layer. It should be noted that when l = 1, the input state of the GCN is the initial state of the neighbor node hj, where hj ∈ H. eij is a normalization constant for the edge  which originates from using the symmetrically normalized adjacency matrix W is a nonlinear parameter matrix.Next, the GCN updates the state information of each monitoring point in layer pi after the graph convolution module, and k + 1 made by the model based on the monitoring point a priori data.The training of a neural network is continuously adjusting the weight matrix while minimizing the error function by back-propagating the gradient. In order to fully utilize the limited data in the dataset, the error profile of the model can be trained to converge by setting multiple epochs. In order to obtain the predictions of the model from the priori data of each monitoring point, we designed a Feed forward neural network (FFNN) immediately following the GCN module as the prediction output layer, and the predicted values are given according to the following functions.y are the predicted and true values of the monitoring points, respectively. Adam is chosen as the optimizer of the model, and the goal of model training is to make the error function tend to be minimized.In terms of the error function, the mean square error of the predicted value and the target value is chosen as the error function in this paper.km from Suizhou City in the east and 52km from Huaibei City in the northeast. Geographical coordinates are 116°23′59′′E ∼ 116°32′04′′E, 33°29′05′′N ∼ 33°33′55′′N. The east-west length is about 10.9km ∼ 13.3km, the north-south width is about 1.3km ∼ 5.3km, the area is about 41.6km2, and the geographical location is shown in m ∼ −1000m elevation, the main mining 72 coal seam, coal thickness 0m ∼ 5.7m, coal mining mainly according to the directional longwall full trap collapse method.The study area of this paper is mainly the 82/83 mining area of Yuandian No. 2 Mine in Anhui Province and the surrounding collapse area in Anhui Province, China, which is located in the northern part of Anhui Province, and its center is about 55To obtain remote sensing image-based subsidence data for the study area, we used remote sensing images taken by the C-band Sentinel-1A satellite of the European Space Agency. From January 2018 to December 2022, 148 SAR images were acquired with an average interval of 12 days, a polarization of VV+VH, and a satellite attitude of ascending orbit phase at the time of photography. Based on the original image data, we use multiple Master-image Coherent Target Small-baseline Interferometric SAR method , 32 to gFurther, in order to obtain more accurate field data, we set up a long-term surface settlement monitoring network with 64 monitoring points in conjunction with the current level points in the mine site. From September 2017 to October 2018, with an average interval of 10 days, the monitoring points were measured at the third-order leveling following China’s “Specifications for the third and fourth order leveling”. Similarly, 20 monitoring points uniformly covering the mining plane of the mine area were selected from the measurement results as a manual data source with 37 consecutive data for each subsidence sequence.Our model is trained on an NVIDIA GeForce RTX 3090Ti with sliding window set to 5, and LSTM module hidden state dimension is 300, and GCN module hidden state set to 300. The experimental data set was split according to 80% as the training set and the remaining 20% as the test set. Too little training does not allow the model to converge, while too much training introduces additional noise. As shown in To verify the accuracy of the model prediction, we use the first 80% data of the time series data as the training set, train the model to predict the subsequent data, and compare it with the real data. Meanwhile, to verify the advancedness of MRF-GCN, we conducted controlled tests using three common time-series prediction models, all benchmark tests used the same data sources, and all models simultaneously predicted future settlement changes at all monitoring sites in the monitoring network.p, q) as  according to the data specifics.Autoregressive integrated moving average (ARIMA)  is one oSpace-time ARIMA (STARIMA)  is a varLSTM  is a praR2 characterizes the degree of correlation between the two data sets, and the mean square error (MSE) can reflect the degree of dispersion between the two data sets. The smaller it is, the better the two data sets match. The experimental results of each model are shown in Finally, the statistical results between all model predictions and the actual values are presented based on the monitoring network data obtained from InSAR images. The Pearson correlation coefficient (PCC) can describe the degree of linear correlation between two matrices, close to 1. It indicates that the two matrices have a strong linear correlation. The R2), respectively. Additionally, MRF-GCN outperforms LSTM by 1.4162mm2 in terms of mean squared error, indicating a closer fit to the actual data. To visualize how the MRF-GCN prediction results match the true values, we select three points from each of the InSAR-based and manual-based area and plot them as time series diagrams, respectively. As shown in Figs p1, p6 and p19 are selected, and for the manual measurement based control network, points p5, p7 and p11 are selected. It should be noted that these points were chosen randomly just to show the prediction effect of the model, and the time series distribution plots of the remaining points can be fully reproduced by the code in this paper. With these line plots, it can be seen that the prediction results of MRF-GCN fit the real subsidence pattern well. Such prediction results can provide credible support for developing subsidence prevention measures.The results in Y = X and Y = X ± 3 on the graph. The red, green, and blue scatter points correspond to the error distribution of MRF-GCN, LSTM, and ARIMA, respectively. It is evident from Y = X line and exhibit a distribution inside Y = X ± 3 compared to the green and blue scatter points. This observation implies that the prediction results based on the MRF-GCN model are significantly closer to the actual value of the data than the traditional time series prediction methods, and the prediction results are relatively more stable. Therefore, it can be concluded that the MRF-GCN model outperforms the traditional prediction methods in terms of accuracy and stability.To thoroughly investigate the accuracy of the model prediction results, we conducted a visual analysis of the error distributions of MRF-GCN and the other benchmark models. To clearly reflect the prediction effect of different models, we randomly selected a point in To provide a comprehensive evaluation of the long-term prediction capability of the proposed model, we conducted a time series comparison between the prediction results of MRF-GCN and LSTM with the actual values of the test set, as illustrated in The application of graphical convolutional networks in modeling the subsidence patterns of multiple points can unveil the interplay among the points. The Pearson correlation coefficient (PCC), which ranges from -1 to 1, reflects the degree of correlation between the two vectors. A higher positive absolute value of PCC indicates a more robust correlation and the same trend direction, and vice versa. Considering the predicted data of each point as a vector, the correlation coefficient between two points can reflect the correlation between their subsidence patterns. To examine whether the model can capture the interdependencies among the points after adding the graph convolution, we use the raw data based on InSAR images to generate predictions by the MRF-GCN and LSTM models, respectively compute the PCC between any two points using the prediction sequence. We present the correlation degrees between different points as a 20 × 20 heat map in p4 is connected to both points p8 and p9. In contrast, p9 is closer to p4 and is located in a stable geological formation, while p8 is farther away and located near water. Therefore, p9 is closer to p4 in terms of subsidence pattern than p8 under the same circumstances. In the heatmap, the MRF-GCN based predictions reflect this actual situation well, with the  grid being significantly darker than the  grid, while the LSTM-based predictions is unable to achieve this well. These patterns can be accurately learned by modeling the neighboring relationship map using GCN. In contrast, the conventional LSTM prediction model treats the variation of each point in isolation and hence cannot model the mutual influence law between the two points.Inspection of R2 value of MRF-GCN model was 0.8650, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.5989, much smaller than that of LSTM and other conventional models. In a restricted view, in order to explore the prediction performance of the model, we ignore the accuracy requirement of remote sensing data and assume that the subsidence data obtained by multiple Master-image Coherent Target Small-baseline Interferometric SAR method meet the actual situation, but this is not allowed in practical applications, so when using this model to predict the actual subsidence area, remote sensing data that meet the accuracy requirement should be obtained. In a word, the model proposed in this paper can well meet the needs of large-scale surface subsidence prediction, and has the potential to be applied to predict surface subsidence caused by various factors.In this paper, we proposed a MRF-GCN model for mining-induced surface subsidence prediction. Unlike previous work, this paper introduced a graph convolutional neural network focusing on the change trend of associated points, and the model can predict the change of current point by learning the subsidence trend of neighboring points. Specifically, to demonstrate how the model works, this paper took the surface subsidence area at the 82/83 mining area of Yuandian No. 2 Mine in Anhui Province as the study area and combined remote sensing technology and manual measurement to obtain subsidence data. These data were used to model with MRF-GCN and to predict the future subsidence trend. The experimental results indicate that the S1 File(ZIP)Click here for additional data file.


Conversely, the indirect pathways through sentiments towards UGS, UGS quality, and time spent in UGS were highly significant , underscoring their substantial role as mediators in the UGS use-health association. While a comprehensive understanding of the mechanisms linking perceived health to UGS use in Mexico City requires further research, this study proposes that fostering positive sentiments towards UGS, enhancing UGS quality, and encouraging extended visits to green areas could potentially amplify the perceived health benefits associated with UGS use among residents. These insights offer valuable inputs for policymaking, emphasizing the importance of integrating public perspectives to optimize nature-based solutions and broaden their positive impact within Mexico City.In recent decades, extensive research has demonstrated the positive impact of urban green spaces (UGS) on public health through several pathways. However, in the context of Latin America, particularly Mexico City, there remains a notable scarcity of evidence linking UGS use to health outcomes and an insufficient understanding of the pathways or factors underlying these associations. Therefore, this study employs Structural Equation Modeling (SEM) to investigate the intricate pathways between UGS use and residents’ perceived health in Mexico City, a densely populated urban center. The SEM integrates three key mediators: sentiments towards UGS, UGS quality, and time spent within these spaces. Survey data was collected through an online survey distributed via social media in May 2020 . The findings indicate a minor yet significant direct link between UGS use and self-reported health (0.0427,  Since the early 19th century, researchers have increasingly recognized the beneficial effects of nature on human health and well-being . This acHowever, specific evidence on the mechanisms linking green space use to improvements in health outcomes in Latin American megacities is lacking, and only a few studies have examined in-depth the pathways or factors mediating some type of relationship between green spaces and health or well-being and evidence has not been conclusive in all cases . For insTo date, little attention has been paid to the effect of using UGS to enhance health outcomes in the densely populated cities of Latin America, where wealth inequalities are vast, and the availability of green spaces is often scarce –26. More2 has experienced accelerated growth rates, exacerbating spatial inequalities that have contributed to increasing public health risk factors [Accordingly, the present study aims to address these gaps by examining the pathways between the use of UGS and the self-reported health of residents of Mexico City, accounting for multiple mediators through Structural Equation Modeling (SEM). In the context of hypothesis testing and theory development, this approach is suitable as it helps in understanding how variables directly and indirectly affect one another. Mexico City was chosen as a case study because it presents patterns of spatial segregation, extreme social inequality, and a dense population of 8,400 people per square kilometer , 36. In  factors –39.As such, this study will seek to determine how the use of green spaces is directly or indirectly associated with Mexico City’s residents’ perceived health through multiple mediators, namely sentiments toward green spaces, UGS quality, and time spent in UGS. The evidence generated will assist the decision-making process regarding the most effective underlying mechanisms to support health through UGS use in the city. The consideration of mediating factors is indispensable in contexts such as Mexico City, where both spatial and social inequalities may influence the perception and use of health-promoting urban environments, as well as the benefits derived from them . The stuIn this study, UGS refers to built environments supporting healthy behaviors, physical activity, recreation, social contact, and overall well-being in cities –44. The Although several studies have linked simple exposure to green spaces, which includes indirect and transitory contact, as a contributor to health –54, thisTo date, several pathways have been suggested between health and UGS use , 59. SinThe first pathway examined in this study is the one mediated through sentiments toward UGS, which were incorporated into the model following Wan and Shen’s (2015)  frameworThe second pathway between UGS use and health explored in this analysis is the one mediated through UGS quality, given its role in affecting green spaces’ type of use and enjoyment –69. MultIn a study conducted in the city of Carmona, Italy, the authors found that the presence of playgrounds, pleasant views, drinking fountains, and recreational areas, all of which are linked to UGS quality, contributed to greater feelings of comfort and better perceptions of green spaces . LikewisLastly, another critical pathway explored in this study links UGS use to health via time spent in UGS. Time spent in UGS also acts as a mediator between UGS quality and health and sentiments toward UGS and health, thus adding two additional pathways between UGS use and self-reported health. Despite some studies indicating that a higher frequency of UGS use is conducive to better health outcomes irrespective of the time people spend in UGS , evidencThe study’s analytical approach relied on an online survey distributed through social media in May 2020. The decision to collect the data via this method was made because of the high risk of COVID-19 contagion through face-to-face data-gathering methods in 2020 . For theThe final questionnaire was then uploaded to Qualtrics for two pilot tests, each involving five volunteers. Following the pilots, the survey was launched on Facebook in May 2020 using targeted advertisements directed at adults (18+) whose profiles indicated that they lived in one of the 16 municipalities of Mexico City. The participants’ location at the time of survey completion was verified through Qualtrics. Individuals outside the study area were excluded from the sample. Furthermore, the ad explicitly sought individuals who had been living in Mexico City for a minimum of 14 months. Facebook was selected as a distribution channel due to its status as the most popular social platform in Mexico, with over 81 million registered users . The finFor the purposes of this analysis, only surveys in which respondents completed all the questions were considered . In order to encourage a higher level of participation, more detailed information like respondents’ specific income was not requested. Instead, respondents were prompted to select their income bracket. This approach aligns with standard practices in online data collection, acknowledging the common reluctance of individuals to provide precise personal information . AdditioStructural Equation Modeling (SEM) was chosen as the primary analytical method due to its capability to accommodate multiple dependent variables and account for mediating effects among interrelated variables , 86. Thi2.2.1.1. UGS use. To assess UGS use and reduce the effect of the COVID-19 pandemic restrictions on respondents’ answers , participants were requested to select the option that most accurately represented their usage patterns of green spaces between March 2019 and March 2020 (spanning 12 months). The survey clarified that "use" specifically referred to direct engagement with these spaces. In other words, respondents were asked about their specific intention to visit green spaces, ensuring that they were not merely passing through them while traveling to another destination. Responses were classified as (i) no use (none), (ii) once every two-three months (rare), (iii) once or twice per month , and (iv) once or more than once a week (frequent). The survey differentiated the frequency of use based on prior research indicating that frequent use of UGS rather than sporadic use is significantly associated with improved health outcomes [outcomes . For exaoutcomes .2.2.1.2. Health. To assess health outcomes, participants were asked to assess their perceived health status utilizing a 5-point Likert scale . This measure of self-reported health was employed as a proxy for health status, aligning with the approach outlined by Jylhä (2009) [ä (2009) . Jylhä’sä (2009) , 90.In Mexico, researchers frequently use self-perceived health as a well-established proxy to study overall health. For example, Valle (2009)  investig2.2.1.3. Mediators. As previously highlighted, this study incorporated three essential mediators: sentiments toward UGS, UGS quality, and time spent in UGS. To assess sentiments toward UGS, participants were requested to evaluate the significance of UGS to their overall quality of life and urban experience using a 4-point Likert scale . Understanding individuals’ sentiments toward spaces is crucial, as this factor significantly influences their inclination to utilize these spaces, subsequently shaping their actual behavior [behavior . Likewisbehavior , 95. Las2.2.1.4. Additional exogenous variables. Two fundamental sociodemographic attributes at the individual level were considered as exogenous variables in the model: age and socioeconomic status. It is well established that an individual’s health and well-being are significantly impacted by their age and socioeconomic circumstances [mstances , 97. Formstances . Similarmstances   emphasized the paramount importance of UGS for both city life and their overall quality of life. However, a notable percentage held contrasting views: 7.1% regarded UGS as unimportant, and 13% considered them only slightly important. This finding underscores that approximately one-fifth of the surveyed individuals did not perceive green spaces as crucial to urban quality of life. Concerning the quality of green spaces in people’s neighborhoods, most individuals felt that this was poor (28%). Intriguingly, the highest rating—excellent quality—garnered the fewest responses, accounting for merely 12% of the participants. This disparity in perceptions sheds light on varying assessments of the local green space quality. Regarding the duration of visits to UGS, a significant majority (69%) reported spending between 16 and 60 minutes per visit. Remarkably, one-fifth of respondents allocated more than 60 minutes during each visit, highlighting the substantial investment of time in utilizing these green spaces.p-value of 0.261. Additionally, crucial fit indices were scrutinized, namely the root mean square error of approximation (RMSEA = 0.012), the comparative fit index (CFI = 1.000), the Tucker-Lewis index (TLI = 0.992), and the standardized root mean squared residual (SRMR = 0.005). All of these metrics affirmed that the model exhibited a strong and favorable fit.To test the total indirect effects of UGS use on perceived health, a bootstrapping method was applied. Many scholars recommend the bootstrapping method over a normal theory approach when testing indirect effects due to its robustness and accuracy . This mep<0.10). Notably, the five indirect pathways explored in the model were highly significant , as outlined in The results obtained through the bootstrapping method indicated a weakly significant direct path from UGS use to self-perceived health , as well as time spent in UGS , as outlined in p <0.01) and moderate significance for time spent in UGS . Interestingly, sentiments toward UGS were not identified as mediators between age or income and perceived health.Among the introduced exogenous variables used as controls, age was found to be significantly associated with respondents’ perceived health both through UGS quality , albeit with a modest effect. Conversely, the direct effect of age on health was moderately significant . Both age  and income  exhibited more substantial direct effects on self-reported health than UGS use , with their total effects being highly significant.In terms of direct effects, income demonstrated a robust association with better-perceived health  significantly influences the degree to which the space’s use is associated with health outcomes , 106.In addition, this study unveils that the time spent in UGS acts as a mediator between sentiments toward UGS and health. In other words, spending more time in UGS is associated with individuals’ positive sentiments toward them. This finding aligns with the results of a study conducted in Turkey by Akpinar (2016) , which eIn order to enhance sentiments toward UGS, public health campaigns to promote a positive perception of UGS and reinforcing their importance for urban quality of life could prove to be a significant contributor to intensifying the relationship between UGS use and perceived , 109. FoThe SEM findings also suggest that the quality of UGS acts as a significant mediator in the pathway between UGS use and self-perceived health, with a higher perceived quality of UGS strengthening this relationship. Veitch et al. (2012) , who stuBuilding upon analogous studies that employed quality as a mediating factor between UGS use and health , it is iBuono et al. (2012)  have higFinally, the findings also suggest that time spent in UGS acts as an additional mediator between UGS use and self-rated health. While some researchers have argued that visiting green spaces is sufficient to benefit from them, others have claimed that spending a certain amount of time in UGS is essential to reaping their benefits –128. ForThe SEM used in this analysis also revealed that the time spent in UGS serves as a mediating pathway between sentiments toward UGS or UGS quality and self-reported health. These findings align with the existing literature, indicating that the quality of UGS is linked to the time individuals spend in these spaces, consequently impacting the health outcomes of the users . SimilarIn this vein, introducing activities that encourage people to spend more time in UGS could prove to be an effective strategy to increase both the frequency and duration of UGS visits . This, iAlthough UGS research has gained traction in recent decades, substantial knowledge gaps persist. Particularly in Latin America, research in this domain has been , 37. ThiIn terms of limitations, it’s vital to stress that SEM, while providing opportunities to examine direct and indirect effects, does not imply causality. Accordingly, causal relationships between variables cannot be inferred from the analyses conducted. Nonetheless, while this study is cross-sectional in nature, it is crucial to emphasize that the findings open intriguing avenues for future research, particularly in terms of UGS quality and sentiments toward green spaces. In addition, the model delves into individual-level variations, acknowledging that external factors, such as high crime rates, could inhibit the utilization of green spaces. Given the structural equation modeling framework of this study, exogenous factors were not specifically investigated.Moreover, it is important to note that certain latent variables that have been shown to significantly mediate the relationship between UGS use and health in diverse studies, such as social cohesion , were noAnother limitation was the use of an online survey, which might have constrained the engagement of marginalized groups. Research has demonstrated that data collection tools relying on the Internet can inadvertently discriminate against underprivileged populations, as individuals without access to the internet at home are automatically excluded . NeverthThis study provided valuable insights into the relationship between the use of urban green spaces and the self-reported health among residents of Mexico City. Employing Structural Equation Modeling, the research effectively addressed notable gaps in existing literature by analyzing the multifaceted connections between UGS use and health outcomes, considering three critical mediators: sentiments toward UGS, UGS quality, and time spent in UGS.The findings shed light on the nuanced relationship between UGS use and perceived health. They emphasize the importance of considering mediating factors to understand the intricate mechanisms through which UGS use influences self-perceived outcomes. While UGS use exhibited a weak and minor direct impact on self-reported health, the indirect pathways were notably robust, collectively bearing more substantive weight. Thus, in Mexico City, enhancing aspects such as quality of UGS, time spent in UGS, and sentiments toward UGS can potentially bolster the positive association between UGS use and self-reported health.Furthermore, the study underscored the need for targeted interventions and thoughtful policy formulation to cultivate favorable perceptions of UGS, elevate UGS quality, and encourage extended periods spent in these spaces. Public health campaigns, engagement initiatives, and activity programs within UGS might be effective tools to achieve these objectives and enhance the public health outcomes associated with UGS use. Nonetheless, additional research on these instruments is needed.Finally, this study is important in its contribution to the discussion on the impact of nature-based solutions on public health in Latin America, where studies are still scarce. As research in this field continues to grow, more evidence is expected to emerge regarding the association between UGS use on health and well-being in Latin American urban settings, and the pathways to strengthen this association. Researchers, policymakers, and urban planners are increasingly recognizing the importance of green spaces in creating healthier and more livable cities in the region. In this regard, this study provides a solid foundation for future studies and policy interventions aimed at optimizing the health benefits derived from UGS and enhancing the overall livability of urban environments, not only in Mexico City but also in similar settings globally.