The receiver operating characteristic (ROC) curve was performed to estimate the prediction value of POR by the following ORTs: anti-Mullerian hormone (AMH), antral follicle count (AFC), basal FSH (bFSH), as well as patient age.A retrospective cohort study was conducted on poor ovary response (POR) patients using real-world data from five reproductive centers of university-affiliated hospitals or large academic hospitals in China. A total of 89,002 women with infertility undergoing their first traditional ovarian stimulation cycle for vs. 0.862), but the improvement was not significant. The AFC with age model significantly improved the prediction accuracy of the single AFC model (AUC 0.846 vs. 0.837). To reach 90% specificity for POR prediction, the cutoff point for age was 38 years old with a sensitivity of 40.7%, 5 for AFC with a sensitivity of 55.9%, and 1.18 ng/ml for AMH with a sensitivity of 63.3%.In this retrospective cohort, the frequency of POR in the first IVF cycle was 14.8%. Age, AFC, AMH, and bFSH were used as predicting factors for POR, of which AMH and AFC were the best indicators when using a single factor for prediction . The predictive values of the multivariate model included age and AMH (AUC 0.865), age and AFC (AUC 0.850), age and all three ORTs (AUC 0.873). Compared with using a single factor alone, the combinations of ORTs and female age can increase the predictive value of POR. Adding age to single AMH model improved the prediction accuracy compared with AMH alone (AUC 0.865 AFC and AMH demonstrated a high accuracy when using ROC regression to predict POR. When testing is reliable, AMH can be used alone to forecast POR. When AFC is used as a prediction parameter, age is suggested to be considered as well. Based on the results of the cutoff threshold analysis, AFC ≤ 5 and AMH ≤ 1.18 ng/ml should be recommended to predict POR more accurately in IVF/ICSI patients. Predicting a patient’s ovarian response prior to the start of the first IVF cycle is important in clinical practice for providing important diagnostic and prognostic value.Poor ovary response (POR) is characterized by a low number of growing follicles and low serum estradiol levels after exogenous gonadotropin stimulation, resulting in a poor oocyte retrieval. POR is associated with poor reproductive outcomes , 2. AccoIn the past few decades, numerous studies have been carried out to measure ovarian reserve through ovarian reserve tests (ORTs) –10. Basain vitro fertilization (IVF) cycles in China to analyze the accuracy of POR prediction by female age and ORTs alone and in combination. The study also explored the cutoff points of key indicators to predict POR and stratified cutoff point according to age.The study benefited from the establishment of a multicenter retrospective database and used a large sample of 89,002 patients who underwent their first This study included the first oocyte retrieval IVF/ICSI cycle of all patients from January 2013 to December 2019 at five reproductive centers in university-affiliated hospitals or large academic hospitals in China including the Sixth Affiliated Hospital of Sun Yat-sen University, Henan Provincial People’s Hospital, Jiangsu Provincial People’s Hospital, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, and Northwest Women’s and Children’s Hospital. The study was reviewed and approved separately by the ethical committees in each hospital, namely, the Reproductive Medicine Ethics Committee of Henan Provincial People’s Hospital (SYSZ-LL-2019110401), Medical Ethics Committee of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology (TJ-IRB20210320), Medicine Ethics Committee of Jiangsu Provincial People’s Hospital (2020-SR-046), Medical Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (2020ZSLYEC-295), and Medical Ethics Committee of Northwest Women’s and Children’s Hospital (2019013). The need for individual consent was waived by the committees due to the retrospective character of the study. Data was desensitized to hide personal information before being processed.The raw data came from the IVF database of the five reproductive centers. We retrieved the desensitization data of patients who underwent IVF/ICSI treatment from January 2013 to December 2019 from each center. The types of raw data collected included hospital admission summary sheet, medical history records of the couples, cycle information, ovulation monitoring, oocyte retrieval records, embryo culture records, frozen and thawing records, transplant records, follow-up records. Data were processed from medical records into standardized research datasets for further analysis.Inclusion criteria: Female patients with regular menstruation and bilateral ovaries at one of the five reproductive centers with first-time fresh cycles of IVF from January 2013 to December 2019 were included in the analysis.Exclusion criteria: Patients with evidence of any of the following conditions were excluded from the study: ① polycystic ovarian syndrome (PCOS) (according to Rotterdam Criteria); ② history of ovarian surgery; ③ history of chemotherapy and pelvic radiotherapy; ④ pretreatment of oral contraceptives within 2 months before conducting the IVF cycle; ⑤ natural cycle IVF and mild stimulation cycle with daily gonadotropin (Gn) <150; ⑥ canceled oocyte retrieval cycle that isn’t due to poor ovarian response.in vitro fertilization cycle. The stimulation protocol and the dose of gonadotropin were determined by the reproductive endocrinologist. In all cases, the dose of gonadotropin was chosen to optimize the number of oocytes retrieved while minimizing the risk of ovarian hyperstimulation syndrome (OHSS).Every patient that met the inclusion criteria underwent the first Before the cycle, venous blood was collected on days 2–4 of the menstrual cycle, and the AFC was measured through a transvaginal ultrasound examination by a reproductive endocrinologist or an experienced sonographer. Within one center, these posts are filled by relatively permanent personnel. Since all the five reproductive centers are large artificial reproductive technology centers of China and each center has its own personnel training and assessment process, thus, the results of the AFC were reliable. AFC is defined as the number of 2–10 mm diameter follicles in two ovaries. After standard venipuncture, the blood sample was completely coagulated and the sample was centrifuged. Then 1 ml serum was removed to a new tube, frozen at 2–8°C within 24 h after blood collection, and tested in an independent laboratory of each IVF center within 2 days. Kangrun Biotech Reagent Automatic SMART6500 immunoassay analyzer was used to detect levels of AMH and sex hormones in serum and plasma samples. The published total imprecision of the AMH assay kit was 2.4–5.2% , 17.POR is defined as the cancelation of the oocyte retrieval cycle due to poor ovarian response or cycles in which the number of oocytes retrieved is three or fewer , 6, 18. POR was designated as the dependent variable , and the following continuous variables were used as independent variables in the analysis: AMH, AFC, bFSH, age. Receiver operating characteristic (ROC) analysis was used to map the sensitivity and specificity of the four independent variables, in order to predict the POR of all possible cutoff points for each indicator. The area under the ROC curve and 95% CI were then described. According to previous studies , 18, theFive large- and medium-sized reproductive centers located in different regions of China  conducted a total of 327,059 IVF/ICSI cycles, of which 145,158 (44.38%) were fresh cycles of first ovulation induction, and at last 89,002 cycles were eligible. Among them, 48,642 cases (54.65%) had AMH test results, 41,702 cases (46.86%) of which used the same detection method ; 85,052 cases (95.56%) had bFSH test results; 88,987 cases (99.98%) were recorded with age; 84,884 cases (58.47%) were recorded with AFC. The specific inclusion and exclusion process of patients is shown in Multivariate logistic regression showed that age was significantly associated with POR with an odds ratio (OR) (95% CI) of 1.050 (1.040–1.059); AFC, AMH, and bFSH were also significantly associated with POR with OR (95% CI) of 0.898 (0.886–0.912), 0.712 (0.672–0.754), and 1.090 (1.073–1.106), respectively (P<0.001) (shown in affiliated table). Age, an independent influencing factor on pregnancy outcomes, was correlated with the other predictors in the study, so we created two models for the prediction of POR. The first models were univariate models for each of the ORTs  and age as predictors separately. The second models were multivariate models that evaluated combinations of each ORT and age together. The different models were used to parse out the predictive value of age and ORTs alone, as well as the added predictive value of age to AMH, AFC, bFSH in combination. ORT parameters combined with age significantly improved the prediction of POR after control ovarian stimulation (COS) (see in Based on the results from the models in vs. AUC 0.862 without age). On the other hand, the age plus AFC model significantly improved the prediction accuracy of the single AFC model (AUC 0.846 with age vs. AUC 0.837 without age). The AUC of bFSH was relatively small in comparison to the other predictors, AUC 0.689 (0.683–0.695), and the predictive effects of single and combined bFSH use were both moderate. Details can be seen in The multivariate analysis of POR prediction showed that the prediction accuracy in the combined model with all predictors AFC, AMH, bFSH, and age was higher than that of the models based on only one parameter. The AUC (95% CI) of the combined model was 0.873 (0.868–0.879). The AUC of the combined model was significantly better than the predicted value of a single parameter, but not significantly better than AMH plus age with AUC (95% CI) of 0.865 (0.860–0.870). The model with AMH alone had the highest AUC (AUC 0.862) among the univariate prediction models; followed by AFC (AUC 0.842). Adding age to the AMH model did not significantly improve the prediction accuracy , which differs from the existing 40 years old cutoff point in the Bologna criteria and 35 years old cutoff point in the Poseidon categories. It was also reported with given evidence from multiple studies that the average rate of follicular depletion, aneuploidy rate, and embryo arrest rate all increase significantly after age 38 –34.A limitation of this study is that although Access AMH with electrochemiluminescence detection was used, variability between different laboratories in each center is still worth exploring. Also, pregnancy outcomes and effective management strategies of POR patients are not referred to. These should be explored on the basis of this research in the future.In conclusion, POR is estimated to occur in 14.8% of the first IVF cycles in the Chinese population. When testing is reliable, AMH can be used alone to forecast POR. When AFC is used as a prediction parameter, age is suggested to be considered as well. AFC ≤5, AMH ≤1.18 ng/ml, and female age ≥38 should be recommended to predict POR more accurately in IVF/ICSI patients.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Henan Provincial People’s Hospital, Ethics Committee of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Ethics Committee of Jiangsu Provincial Hospital, Ethics Committee of Northwest Women’s and Children’s Hospital, and Ethics Committee of Sixth Affiliated Hospital of Sun Yat-sen University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.C-LZ and X-YL supervised the entire study, including the procedures, conception, design, and completion, and participated in the interpretation of the study data and in revisions to the article. LJ, J-ZS, Y-DM, and RH were responsible for the collection of data. XW contributed to the data analysis and drafted the article. Y-NJ wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.http://www.nsfc.gov.cn/), and National Key R&D Program of China (2018YFC1002106).The study was funded by National Natural Science Foundation of China (No. U2004130 The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Connectivity matrices for each epoch for each FC function were built, and graph theory was used to obtain the following metrics: strength, cluster coefficient, betweenness centrality, eigenvector centrality , and global efficiency. The statistical distributions of these metrics were compared among the three classes, using ANOVA, for each FC function. We found significant differences in all global (p < 0.001) and local (p < 0.00002) graph metrics of the far class compared with before and mid for motif synchronization on the beta band; local betweenness centrality also pointed to a degree of lateralization on the frontotemporal structures. This analysis demonstrates the potential of FC measures, computed using motif synchronization, for the characterization of epileptiform activity of MTLE patients. This methodology may be helpful in the analysis of EEG-fMRI data applied to epileptic foci localization. Nonetheless, the methods must be tested with a larger sample and with other epileptic phenotypes.Over the last decade, several methods for analysis of epileptiform signals in electroencephalography (EEG) have been proposed. These methods mainly use EEG signal features in either the time or the frequency domain to separate regular, interictal, and ictal brain activity. The aim of this work was to evaluate the feasibility of using functional connectivity (FC) based feature extraction methods for the analysis of epileptiform discharges in EEG signals. These signals were obtained from EEG-fMRI sessions of 10 patients with mesial temporal lobe epilepsy (MTLE) with unilateral hippocampal atrophy. The connectivity functions investigated were motif synchronization, imaginary coherence, and magnitude squared coherence in the alpha, beta, and gamma bands of the EEG. EEG signals were sectioned into 1-s epochs and classified according to (using neurologist markers): activity  Epilepsy is a neurological disease that affects around 50 million people worldwide. The World Health Organization estimates that 75% of people with epilepsy in low- and mid-income countries do not receive treatment. The risk of a premature death is three times higher for individuals with epilepsy compared to that of the general population. Among adults, depression and anxiety are more often recurring in people with epilepsy . Mesial The diagnosis and characterization of an epileptic condition is usually done by visual inspection of the electroencephalography (EEG) exam by an expert neurologist/neurophysiologist. This is a time-consuming and difficult task. Furthermore, “manual” EEG characterization is not a standardized process as it is highly dependent on the experimental setting and particular choices of the inspecting neurophysiologist.Automated techniques for aiding EEG epilepsy characterization are more than three decades old . AlthougModern seizure/IED detection approaches frequently use machine learning algorithms  to classify healthy/ictal, interictal/ictal, or postresection/ictal signals. To that end, these methods follow a pipeline that roughly consists of the same five steps: (1) measurement, (2) preprocessing, (3) feature extraction, (4) feature selection, and 5) classification  classifi, the autOn the other hand, some works explore the functional connectivity (FC)  among brThe use of FC in epilepsy studies is a growing trend, even for seizure detection, as shown in , 17–19. far from IEDs, activity immediately before an IED, and finally, mid-IED activity. Therefore, this work focuses on the third step of the modern seizure/IED detection approaches aforementioned; namely, feature extraction. At this stage, we did not yet attempt to look at feature selection nor at classification.On the other hand, the concomitant acquisition of EEG and fMRI data has been shown useful to characterize different forms of epilepsy and to help in the surgical evaluation of pharmacoresistant patients –25. UsuaOur institution has an EEG-fMRI data set of epilepsy patients undergoing presurgical evaluation. This data set is on continuous expansion and counts data from more than 100 patients. In an attempt to standardize the current analysis, data were selected from 10 patients with MTLE, diagnosed according to ILAE criteria , and haBrainCap MR cap (one of them being an ECG channel) positioned according to the 10/10 system, Ag-Cl electrodes, two Brain Amp MRplus amplifiers , sampling rate of 5 kHz, and the BrainVision Recorder 1.20 software. EEG data went through the following preprocessing steps: (1) removal of artifacts, including those from magnetic resonance gradients; (2) down-sampling to 250 Hz; (3) application of a 15-Hz low-pass filter to the ECG channel; (4) ballistocardiogram artifact correction for the other channels; (5) application of a bandpass filter between 0.5 and 70 Hz and a notch filter at 60 Hz .EEG signals were acquired during fMRI sessions with an average recording time for each patient of 35 min. These sessions were split into either multiple 6-min sessions or two 22-min sessions. The EEG signals were measured with a 64-channel Expert neurophysiologists marked epileptiform events in all signals according to the following graphoelements: sharp waves, spikes, polyspikes, temporal intermittent rhythmic delta activity (TIRDA).before , mid , and far . This epoch duration (1 s) was chosen because of the limitations of our connectivity techniques, as is explained further. From these markings, epochs of 1 s duration were obtained from the EEG signals, according to the following denomination: For each epoch, FC matrices were computed using three different methods: MSC, IC, and motifs. The goal was to compare graph properties of these techniques during ictal and interictal activity.The MSC and IC functions work on the frequency domain. MSC was calculated in the range 8–59 Hz in 4 Hz sized bins and with 50% overlap. These bins were averaged in bands: alpha (8–12 Hz), beta (13–29 Hz), and gamma (30–59 Hz). The IC function was calculated for the same frequency interval with same-sized bins (4 Hz) and same frequency bands. For the MSC and IC, the EEG sampling rate during acquisition is a limiting factor for frequency resolution and bin size. Therefore, the length of the epochs was set to 1 s to optimize the time resolution of these techniques for the chosen frequency bands on the EEG signal with a 250-Hz sampling rate. The third technique, motifs, was applied to alpha/beta/gamma bandpass-filtered versions of the EEG signals. Then, each 1-s epoch of brain activity generated nine FC matrices (three FC functions × three bands).i and j) in the frequency domain. MSC is calculated as follows  or independent components (MSCij[f] = 0).Using MSC, it is possible, for instance, to assess if two signals have completely linearly dependent frequency components .where x(t); any given point x(tk) follows some pattern in relation to its neighborhood. For instance, when comparing x(tk) to its next two neighbors (n = 3), and considering that, in a continuous distribution, it is highly unlikely that these neighboring points assume equal values, one could find that x(tk)< x(tk+1)< x(tk+2) or maybe that x(tk)> x(tk+1)> x(tk+2) . Given labels, these patterns become motifs. Equation (3) represents the six motifs for n = 3, also shown in The motif analysis process can be considered a type of synchronization likelihood FC measurement , 29. As xM(tk) associated with each point x(tk) (see LM of a motif series is given by LM = L − (n − 1), where L is the length of the time series and n is the length of the motifs.A motif series is constructed from a time series by taking the motif (tk) see . The lenx(t) and y(t) are two simultaneous time series, xM(t) and yM(t) represent their corresponding motif series. As such, two possibilities can be considered when looking at a time tk:Motif synchronization is a measure of similarity between two simultaneous motif series. If cij(tk) is the similarity coefficient for time tk.where Lτ), the synchronization (or similarity) degree is given by the average:For a time window of duration τ . Bonferroni correction for multiple comparisons was applied , giving a significance level All patients were diagnosed with MTLE with signs of hippocampal sclerosis on MRI . The mean age of the patients was 35 ± 11 years, seven were female. p < 0.001) between classes  using the ANOVA test with Bonferroni correction. These were all the global metrics  between classes , using the ANOVA test with Bonferroni correction. Only the motif method applied to the beta band presented significant results; therefore, results for the other bands and other FC methods are not shown. From this table, we see that local strength and local cluster coefficient were significant for all electrodes; local eigenvector centrality was significant for most electrodes with the exception of C4, Cz, FC2, C2, TP8, and CPz; and local betweenness centrality was significant only for electrodes F7, T7, FC1, FC3, FC5, FC6, F5, C6, and TP7.This is an exploratory study whose main objective was to characterize interictal epileptiform activity  from EEG data using graph measures. For this, three different methods for computing FC were explored: MSC, IC, and motif synchronization, which provided connectivity matrices for three frequency bands each: alpha, beta, and gamma. The main innovative aspects of this work are the use of the motifs method to evaluate the FC from EEG data and the use of graph measures for the characterization of epileptic activity. To the best of our knowledge, the motifs method has not yet been used in this context. Our graph analyses inspected four local metrics  and five global metrics . We wanted to look at the networks in order to assert whether they could provide useful information to detect IEDs in the EEG signal.p < 0.001) and locally (p < 0.00002). Neither the other FC methods (MSC and IC) nor the other frequency bands  were able to separate the classes. Unfortunately, none of the methods was able to separate the before and mid classes, which seems to point to the fact that, FC-wise, the EEG signal in the 1-s segment just before an IED is very similar to the signal during an IED.We looked at EEG epochs during IEDs (mid), just before an IED, and far from IEDs. Interestingly, only the motif method for the beta band provided metrics that were able to distinguish far epochs from mid and before segments, both globally  or almost all  nodes . BetweenAs the application of the motifs method seems to be a novelty in this context, some considerations are necessary. The motifs technique does not evaluate amplitude variation, only trends in variation. As such, it might be more sensitive to volume conduction effects than the IC method. This, surprisingly, might be a good feature of the motifs technique in short temporal resolution settings for the problem at hand.Regarding the clinical contribution of this work, it is important to note that EEG data with epileptiform discharges is useful for epilepsy diagnosis, to define the epilepsy subtype (according to the epileptiform discharges distribution), and in cases of epilepsy surgery, for investigation to localize the epileptogenic zone. In this context, the obtained relation between the IED EEG segments and EEG brain connectivity might be helpful to localize the epileptogenic zone when adding information from the fMRI data.This work has several limitations. In the first place, the analyzed patient sample is small. It would be important to include a larger number of patients and also patients with other types of epilepsy . However, the aim of this work was to perform a methodological survey, i.e., to evaluate the feasibility of using graph measures arising from different methods to distinguish among the mentioned types of EEG segments. At this stage, we did not intend to make inferences about a population. Lower frequency bands (such as delta and theta) were overlooked, so future analyses should include them. For an eventual seizure prediction scheme, we should also expand the range of the “before IED” segments (as they were only 1-s long epochs of activity) to at least 10 s. In addition, in this work, we did not look at feature selection nor at classification, which we intend to do at a future stage.Also, because we included patients with long-term epilepsy resistant to antiseizure medication, all patients were under treatment for a long time. Thus, the influence of antiseizure medications on brain connectivity is a limitation of our study because it is inherent to the situation, given that it would not be ethical to evaluate patients with active epilepsy without drug treatment. Considering the few patients included, the analysis of the possible effect of each medication separately in brain connectivity was not possible.Finally, the EEG data used were acquired concomitantly with fMRI data. Although EEG data acquired inside an MRI scanner may suffer from additional artifacts (such as gradient-based), which can impair the information-extraction process, the idea was to, in future work, use an EEG automated analysis based on FC measures to identify epileptic markers that, in turn, could be used in the event-related analysis of the fMRI data for epileptic focus identification. This work represents the first step toward that goal. Indeed, the EEG-fMRI technique has been largely used to help in the presurgical evaluation of focal epilepsy patients –26.This work aimed to assess the potential of FC based on graph analysis to characterize IED events in EEG data obtained concomitantly with fMRI recordings. Although, at this stage, MRI data was not incorporated into our analysis, we intend to do so in future works. At this point, the methods used here could be applied to common scalp EEG as well. Three similarity measures for computing FC were evaluated, namely, MSC, IC, and motifs. Although coherence-based techniques (such as MSC and IC) have been more commonly applied to EEG signals, the motif synchronization method, applied to the beta band, was the one that showed more potential for differentiating global and local graph metrics from IEDs (and the moment preceding them) from EEG segments far from these events. From the graph metrics evaluated, four out of five that were significant here agreed with previous works. Future analyses should include data from more patients and lower frequency bands as well. Future steps include an implementation of the feature selection and classification stages with machine learning algorithms to make a tool for aiding IED detection. Also, for eventual seizure prediction tools, it would be interesting to observe larger segments (epochs) preceding IEDs/seizures.Finally, the method used in this paper is a data-driven method, which can be useful in EEG-fMRI analyses for epileptic focus identification. EEG-fMRI is a promising technique that adds relevant clinical information to the diagnosis and preoperative evaluations of epilepsy patients.brunno@unicamp.br.The raw data supporting the conclusions of this article is private and cannot be made available for the next few years. Requests to access the datasets should be directed to Dr. Brunno Machado de Campos, The studies involving human participants were reviewed and approved by Comitê de Ética em Pesquisa. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.LC performed the analyses and helped with the analysis design and wrote the paper. GC conceived and designed the analysis and wrote the paper. BC and MA acquired the data, performed the clinical evaluation and much of the pre-processing, and contributed to the paper. All authors approved the submitted version of the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The research hypotheses included secure workplace attachment style as independent variable, OCBs as the dependent variable, and perceived comfort and relations with patients as moderators. Results showed that both secure workplace attachment and perceived comfort promote OCBs, but the latter counts especially as a compensation of an insecure workplace attachment. As expected, difficult relationships with patients hinder the relationship between secure workplace attachment style and OCBs. In sum, our study highlights the importance of the joint consideration of the psychological, social, and environmental dimensions for fostering positive behaviors in caregivers employed in elderly care settings.This study focuses on caregivers who work in residential facilities (RFs) for the elderly, and specifically on their organizational citizenship behaviors (OCBs) in relation to their interaction respectively with the overall context (workplace attachment dimension), the spatial-physical environment , and the social environment (relationship with patients). A sample of health care workers (medical or health care specialists, nurses, and office employees,  The topic of Active and Healthy Aging (HAA) from a psychological perspective ,2,3,4 isRFs approach the issue of HAA from two complementary perspectives. The first concerns the structuring of therapy and rehabilitation pathways  post hospitalization and post inpatient. These are aimed at restoring the dimensions of health or maintaining and enhancing the residual ones, in a bio-psycho-social perspective . These aIn the pursuit of HAA, particularly with the most frail elderly and those destined to long-term inpatient stays, RFs are supposed to be oriented toward achieving and maintaining high standards of response quality and healthcare humanization ,13. ThisThe importance that RFs have assumed in recent years, as a positive NHS response to the need for care and pursuit of HAA, is evidenced by the recent COVID-19 pandemic due to the SARS-CoV-2 virus . In ItalThe psychological literature on HAA has primarily focused on the elderly people, whereas less attention has been devoted to caregivers as key resources for the promotion of elderly health in institutionalized settings ,28. The According to Organ , organizRegarding healthcare organizations, several studies show the importance of the role played by OCBs. For example, Jahani et al.  showed tIn contrast, there are very few studies regarding OCBs of caregivers who work in RFs with the elderly. For instance, a study was led on caregivers of long term and community care facilities for the elderly by Ginsburg et al. , who higThe development of positive job-related responses in workers can be also promoted by a positive workplace attachment, which is a specific pattern included in the broader construct of place attachment, defined as an individual’s bond (mainly of affective nature) to a meaningful place . Place aThus, a workplace that meets the employee’s needs should allow the employee to develop a secure attachment style with the workplace. The development of a secure attachment style to the workplace seems to have numerous beneficial aspects. For example, a secure style promotes exploration, since this allows individuals to walk away without feeling discomfort . AccordiThe literature on the effect of the workplace spatial-physical features on office employees’ well-being and behaviors has evidenced that design-related dimensions such as noise, lighting, temperature, and indoor air quality conditions can play a role in employees’ satisfaction, attitudes, behaviors, and performance . The laIn the perspective of the humanization of care settings, a patient-centered approach, takes on particular importance. This orientation is focused on the involvement of the patient as an active and autonomous subject , and onThe aim of this research is to verify the role of secure workplace attachment style , perceived comfort  and relationship staff-patients (regarding people-people interface) in the RFs workers’ OCBs.The relationship between workplace attachment and OCBs , is still substantially unexplored, with a few exceptions. For instance, Desivilya  showed tHypothesis 1 (H1).Secure workplace attachment is positively related to OCBs, both in terms of overall factors and concerning the distinct OCBs components .Studies on the influence of physical-spatial comfort and organizational behaviors such as OCBs are generally lacking. Even though it has been shown that office physical features such as noise, lighting conditions, temperature, and indoor air quality impact the attitudes, behaviors, productivity, and satisfaction of employees , and thaWe expected both a main effect of perceived spatial-physical comfort on OCBs and an amplification effect of the relationship between secure workplace attachment and OCBs:Hypothesis 2 (H2).Perceived spatial-physical comfort is positively related to OCBs, both in terms of overall factors and concerning the distinct OCBs components.Hypothesis 3 (H3).Perceived spatial-physical comfort moderates the relationship between secure workplace attachment and OCBs, both in terms of overall factors and concerning the distinct OCBs components.Regarding the bond of staff-patient relationships with OCBs or similar behaviors, Jarfarpanah et al. , found aAs well as in the case of perceived spatial-physical comfort, we hypothesized both a main negative effect of difficult staff-patient relationships on OCBs and a buffer effect of the link between secure workplace attachment and OCBs. Thus, we expected that:Hypothesis 4 (H4).Difficult staff-patient relationships are negatively related to OCBs, both in terms of overall factors and concerning the distinct OCBs components.Hypothesis 5 (H5).Difficult staff-patient relationships moderate the relationship between secure workplace attachment and OCBs, both in terms of overall factors and concerning the distinct OCBs components.Data were collected between October 2019 and February 2020 through a self-report paper-pencil questionnaire administered to operators of three RFs in the Autonomous Region of Sardinia . The sample was of convenience and those who participated in the research volunteered (without receiving any compensation). Although the sample is not representative of the population of RFs caregivers, we respected the structure of job roles found in RFs. A total of 164 research questionnaires were distributed, of which 129 were returned (response rate = 78.65%).n = 100), whereas males are about one quarter . Approximately half of the participants  are health care workers ”); 22.48% (n = 29) are medical or health care specialists; 13.95% (n = 18) are nurses; and about the remaining 10% are office employees (n = 13). Most are either graduates  or high school graduates  and have an average length of service of 11.18 years (SD = 6.95). Finally, about half are between the ages of 31 and 45  and about one-third are between the ages of 46 and 60 .The final sample of 129 practitioners consists mostly of females : We used the brief version (15 items) of the Organizational Citizenship Behavior Scale [or Scale  in its Ior Scale . The PriSecure workplace attachment style: from the Workplace Attachment Scale , recentil–EALT, , the fivPerceived physical-spatial comfort: A 16-item scale adapted from the short version of the Perceived Healthcare Environment Quality Indicators  was  was PercDifficult relationship with patients: for the assessment of the relationship between RFs workers and patients, the five items of the component “ambiguous customer expectations” of the Customer-Related Social Stressors scale by Dormann and Zapf [and Zapf  were useFor all these measures, the response scales consisted of a 7-step Likert-type scale (from 1 = “completely disagree” to 7 = “fully agree”).Socio-demographic data  were also collected.2/df = 1.6).The mono-factorial structure of the scales measuring secure workplace attachment style, perceived spatial-physical comfort, and difficult relationship with patients was verified through a Principal Component Analysis (on IBM SPSS 26). As concerns the factorial structure of the OCBs scale see , which hTo test the research hypotheses, model 2 b of the The simple slope analysis allowed us to interpret the moderation effects of perceived comfort and difficult relationship with patients. Sex, age, education level, and marital status were inserted as covariates.The research was authorized by the Ethics Committee of the University of Cagliari . It was conducted in full compliance with the Ethical Principles of Psychologists and Code of Conduct of the American Psychological Association (APA), integrated into the Associazione Italiana Psicologia (AIP) code of ethics. Furthermore, the study did not address any sensitive topics and was carried out via procedures for informed and consenting adults. Lastly, in accordance with the Italian privacy law, the research ensured the anonymity and privacy of all participants.The results of the factor analysis on the multidimensional scales suggested the use of a single-factor measure for the scales of perceived comfort, secure workplace attachment style, and problematic relationships with patients; and a 3-factorial structure for the OCB scale .p < 0.01) and marital status  are negatively associated with Altruism, while education level is positively correlated with perceived comfort . As expected, overall OCB correlates positively with secure workplace attachment style . Specifically, secure workplace attachment correlates with Altruism  and with Civic Virtue , but not with Conscientiousness . Furthermore, overall OCB correlates positively with perceived comfort . The three dimensions of OCB, Altruism, Conscientiousness and Civic Virtue, are positively associated (p < 0.0001) with perceived comfort, showing coefficients of 0.35, 0.33, and 0.30 respectively. It is also interesting to note that no OCB dimension is associated with difficult relationship with patient. Finally, secure workplace attachment correlates positively with perceived comfort Regarding the correlations between socio-demographic variables and the variables under study, age  (p < 0.05) (p < 0.01) . As expected (H1), secure workplace attachment style is positively related with overall OCB  , Altruis < 0.05)  and Civi < 0.01) .p = n.s.)  .p < 0.01) (p < 0.01) (p < 0.01) (p = n.s.) : the perceived comfort was positively related with overall OCB  , Altruis < 0.01)  and Cons < 0.01)  but not  = n.s.) .p < 0.05) (Unlike what was expected (H3) only the interaction between secure WA and perceived comfort on overall OCB was statistically significant  (p = n.s.) (p = n.s.)  , Conscie = n.s.)  and Civi = n.s.) .p = n.s.) (p = n.s.) (p = n.s.) (p = n.s.)  , nor wit = n.s.) , Civic V = n.s.)  and Cons = n.s.) .p < 0.01) (p < 0.05) (p < 0.01) (p < 0.01) (The results referring to the interaction between secure WA and difficult relationship with patients fully support the hypotheses (H5): as emerged from the simple slope analysis, the interaction is negatively associated with overall OCB , especiAbout H2, it is substantially confirmed. In fact, in line with Carter et al. , caregivConcerning H3, a pattern opposite to the one expected was found. In fact, perceived comfort played a buffering rather than an amplifying role in the moderation of the relationship between secure workplace attachment style and OCBs. It is to note that this was found only for the overall OCBs and not for the specific components. This counterintuitive finding, which requires further investigation, can be explained in terms of compensation provided by a comfortable environment for those caregivers with an insecure attachment to the workplace, whereas this environmental perception counts less in case of secure workplace attachment. In other words, the fact that operators perceive a condition of physical-spatial comfort overshadows (and depowers) the need for them to “draw on” their secure workplace attachment style as a motivational dimension capable of promoting their OCBs.Regarding H4, contrary to what was expected, the difficulties faced by staff in their relationships with patients do not negatively affect their implementation of OCBs. A possible explanation for this lack of negative incidence can lie in the awareness that RFs operators have of the particular conditions, non-self-sufficiency, and often mental pathology of their patients . This awareness can lead practitioners to a high level of tolerance and compassionate forbearance of the relational difficulties that may arise with these type of patients , withouFinally, H5 is confirmed, since the difficulties in the relationships with patients adversely affect the relationship between caregivers’ secure workplace attachment and their OCBs . This result suggests that it is also important to consider the relational dimension of the secure workplace attachment style, both towards the social work environment  and towThis study has some limitations. The first one concerns the limited number of participants, due to the specific target population , that is not easy to reach and investigate for its special characteristics (including the difficulty to obtain access to elderly facilities for survey aims). In addition, participants were identified based on their availability, without a sampling method that would allow us representativeness of these workers and generalizability of our results. However, the sample number is good enough for ensuring the reliability of our results, given the low number of variables that we included in the tested moderation model. A second limitation is given by the lack of objective parameters of the design features, since we relied only on the staff assessment of their spatial-physical environment. Thus, future research should fill this gap by considering also objective indicators of the workplace environment, such as spatial configuration, lighting conditions, indoor and outdoor views, furniture, etc., to compare with users’ perceived comfort. Finally, it would be interesting to confirm our speculations about why the independent or moderating variables in our study did not affect OCBs (as for H4) or affected them in an unexpected way  through the realization of a qualitative exploration  of practitioners’ views.Despite these limitations, the outcomes of our study emphasize the importance of the joint consideration, for the implementation of OCBs in the RFs, and more generally in residential care settings, of dimensions tapping aspects concerning different socio-psychological interfaces, i.e., covering the overall context , mirrored by the workplace attachment style; the spatial-physical environment, represented by the perceived environmental comfort; and finally the people facet, reflected by the relationships between staff and patients. In other words, this study highlights that, in order to promote positive caregiver behaviors (OCBs) towards older adults and the healthcare organization, it is crucial to jointly consider place-based dimensions, such as the workplace attachment style and the perceived environmental comfort, as well as the socio-relational dimension represented by the helping relationship towards patients.Our study lies within the theoretical framework of positive organizational behaviors (POBs) enacted by individuals, teams, organizations, and the health network as a whole ,99, in t


We report the experimental implementation of optically-powered wireless sensor nodes based on the power-over-fiber (PoF) technology, aiming at Industrial Internet of Things (IIoT) applications. This technique employs optical fibers to transmit power and is proposed as a solution to address the hazardous industrial environment challenges, e.g., electromagnetic interference and extreme temperatures. The proposed approach enables two different IIoT scenarios, in which wireless transmitter (TX) and receiver (RX) nodes are powered by a PoF system, enabling local and remote temperature data monitoring, with the purpose of achieving an intelligent and reliable process management in industrial production lines. In addition, the system performance is investigated as a function of the delivered electrical power and power transmission efficiency (PTE), which is the primary performance metric of a PoF system. We report 1.4 W electrical power deliver with PTE = 24%. Furthermore, we carry out a voltage stability analysis, demonstrating that the PoF system is capable of delivering stable voltage to a wide range of applications. Finally, we present a comparison of temperature measurements between the proposed approach and a conventional industrial programmable logic controller (PLC). The obtained results demonstrate that PoF might be considered as a potential technology to power and enhance the energy efficiency of IIoT sensing systems. The fourth industrial revolution, so called Industry 4.0, is defined as the integration of complex physical automation systems composed by machinery and devices connected via sensors, which are controlled by software, aiming to improve process performance and reliability . Key feaThe concept of using IoT technologies in manufacturing, known as Industrial IoT (IIoT), is considered a potential technique to enable Industry 4.0 scenarios, providing interconnection of engines, power grids, and sensors to the cloud in the industrial environment. This connectivity is extremely important to allow that all data collected in the complex industrial physical environment is available real-time for the software applications in the network top layers. Therefore, the integration of IIoT and Industry 4.0 enables robust, faster, and most importantly, secure systems. However, the industrial environment brings some challenges for IIoT implementation, namely high dynamic environment, severe electromagnetic interference levels, high environmental temperature, and explosive and hazardous areas ,6. Furthpower-over-fiber (PoF) technology might be a potential solution to supply the required power to remote devices and sensors in industrial environments. This technique, firstly reported in 1978 ,11. In tRecently, the PoF technology has greatly improved due to the advance of the photonics components. Power levels have increased from less than 1 W to over 40 W in the last 20 years . In addiIn particular, Wang et al.  reportedwas 2.5% . López-Cwas 2.5% . Moreovewas 2.5%  demonstrwas 2.5%  employedTo the best of the authors’ knowledge, this work presents an unprecedented system performance comparison between a PoF-based sensing system with a conventional industrial programmable logic controller (PLC), with the purpose of demonstrating the feasibility and potential of this approach for real IIoT scenarios. The evaluation methodology consisted in varying the temperature in a controlled environment and analyzing the data acquired from the optically-powered nodes and PLC. Furthermore, the PoF system is evaluated in terms of the delivered electrical power and overall efficiency. A voltage stability analysis is reported for the first time in the literature in order to demonstrate the flexibility and applicability of our PoF-based sensing system. The manuscript is organized as follows. In contrast to IoT, which is aimed at consumer usage, IIoT is used for industrial purposes, e.g., manufacturing, supply chain monitoring, and management systems. Therefore, IIoT can be characterized as the connection of wireless sensor networks (WSNs) and wireless sensor and actuators networks (WSANs) in industrial environments aiming to collect data from different sensors, actuators and machines, focusing on transfer and control of critical mission information, which relies on machine-to-machine M2M communications ,22. In aCommunication technologies are the base of IIoT, since manufacturing data sensed is transmitted and exchanged between different communication terminals to realize the interconnection of heterogeneous production factors. Communication technologies in network and application layers can be divided into wired, which include fieldbus and Ethernet technologies and wireless, which typically employ ZigBee, Wireless Fidelity (Wi-Fi), fourth-generation of mobile networks (4G), fifth generation of mobile networks (5G), and others .In particular, industrial fieldbus technology is mainly used to provide communication between sensors, instruments, actuators, and other underlying field devices and perform the data transmission between the devices with upper level control system. Typical representatives are controler area network (CAN), ProfiBus and HART. On the other hand, industrial Ethernet aims to realize vertical integration between management systems and shop-floor, through the integration between the Ethernet transmission control protocol (TCP)/Internet protocol (IP) and industrial fieldbus. As a consequence, new protocols take place, as Modbus TCP, ProfiNet, and Ethernet/IP. A wide range of bus communication technologies provides reliable guarantee for communication between heterogeneous production factors in shop-floor and the upper control system. However, cross communication between different shop-floors mainly depends on the traditional Internet, which has the disadvantages of large delay, low security, and complex line layout .WSNs have been widely explored in the literature and are considered as an effective approach to achieve ubiquitous connectivity in IIoT environments. Wireless communication allows users to easily connect mobile and inaccessible devices, simplifying line layout and reducing costs. Several wireless communication technologies have been employed in IIoT, including small-area wireless communication technologies as Wi-Fi, Zigbee, IPv6 over Low-Power Wireless Area Network (6LoWPAN), Bluetooth, and large-area wireless communication technologies as 4G and 5G. Small-area wireless communication technologies are not suitable for the wide range of IIoT. However, transmission rate, node connection, time-delay, reliability and security of the current large area wireless communication technologies are not enough to meet the demands of future IIoT .The IIoT implementation also enables cyber physical manufacturing system (CPMS), which is designed to achieve smart management in manufacturing physical spaces, such as smart planning, production factor optimization, shop-floor optimization, production scheduling and inventory management . Some fuThe use of IIoT in hazardous environments is a key aspect to improve control, instrumentation and monitoring functionalities in automation and control systems. In this context, the use of IIoT technologies has increased considerably on the last few years, mainly in critical areas, where a secure operation system is mandatory . Thus, tThe PoF technique basically consists of delivering electrical energy to a device at a remote location through an optical fiber ,38. FiguThe amount of delivered electrical power is determined by the HPLD output power, PPC efficiency, and optical fiber characteristics such as attenuation and length. The power budget of a generic PoF link is given by:One may note that the main challenges regarding the implementation of a PoF system are related to the HPLD output power, total optical fiber link attenuation, and the PPC conversion efficiency. The HPLD output power depends on the operating wavelength and electrical-to-optical (E/O) conversion efficiency. In addition, fiber type and length have great impact on the total link attenuation.The HPLD is responsible for generating the optical power, which will subsequently be converted into electrical power. The HPLD operating wavelength and conversion efficiency are major concerns in PoF systems and mostly depend on the composition of the material. In particular, HPLDs commonly operate in the range of 808 to 980 nm ,39. The % to 50% .2 [The main purpose of the optical fiber in a PoF system is to transmit as much power as possible from the HPLD to the PPC . In thisgiven by :(3)Pth=I5 MW/cm2  and Aefffined as :(4)Aeff=th bands ,44,45,46 density . Other f density ,47,48.The PPC enables the conversion of the input optical power into electrical power and, therefore, is a critical component in a PoF system. In contrast to the conventional solar cells, PPCs must be compact to match the beam diameter generated from HPLD. Typically, PPC is made from a miniaturized circular solar cell array, usually 2 mm × 2 mm, having a circular aperture diameter of 1.5 mm. In addition, this component is typically designed to support very high-density optical power, contrarily to the conventional photovoltaic systems ,37,49. T% to 60% ,40.The power transmission efficiency (PTE) is one of the key performance metrics in a PoF system. It is generally defined as the ratio between the HPLD output power and total electrical power delivered by the PPC or as defined by :6)PTE=PPTE=P(6)PThis section describes the PoF implementation, envisaging its application to optically power wireless nodes aiming to enhance the control process and failure prevention in an IIoT environment. The PoF technique has been used to enable two distinct industrial scenarios, in order to demonstrate its flexibility to meet multiple applications. In the first scenario, the PoF system powered two IoT TX nodes, composed of temperature sensors, control boards and wireless TXs, establishing the connection with the RX node. The second scenario consisted in powering the IoT RX node, validating the PoF technique applicability for optically powering TX and RX IoT nodes. The system implementation analysis has been divided into three parts, namely PoF system description and analysis, first scenario, and second scenario implementation.Generally, PoF systems are composed of a HPLD followed by an optical fiber link and a PPC. In particular, our PoF experimental implementation, illustrated in Constant supply voltage and current are crucial parameters to ensure reliable and accurate performance of industrial sensing systems. In this context, we have compared the PoF system output voltage considering two scenarios: conventional DC supply (MPL-3305M MINIPA) and PoF system providing 5 V and 8.5 V, respectively. In the first case, we have employed a DC/DC converter in order to meet the requirements of the first scenario. on (NFC) . The modThe first step was to power only the TX node 1, in order to evaluate the PoF system capabilities. In this scheme, over 0.6 W optical power was transmitted from the HPLD to the PPC. Considering fiber and splice attenuation, the total PoF link loss could be estimated in approximately 1 dB. Therefore, over 0.5 W optical power was injected into the PPC, which presents a conversion efficiency of approximately 30%, resulting in a delivered electrical power of around 152 mW. Finally, we measured the TX node 1 consumed power at the DC/DC converter output, which resulted in approximately 140 mW. Consequently, PTE of around 23% could be achieved with this configuration.The control board 1 was responsible for processing and sending the acquired temperature data to the wireless TX 1, which transmitted the data every 100 ms. The wireless propagation was performed over a 10 m link at 2.4 GHz, attaining 2 Mbps and 0 dBm of output power, in a laboratory environment composed of computers, tables, glass doors, and pieces of equipment, characterizing a non-line-of-sight (NLOS) environment. The wireless link is relatively low-power, meaning the link distance between the TX and RX nodes is limited to a few meters. Nevertheless, longer-reach wireless links could be achieved by increasing the transmission power and RX sensitivity. In addition, the measurements have been obtained during business hours, with researchers and students at the lab, characterizing a realistic wireless scenario. At the reception side, the RX node, which consists of an identical control board and wireless transceiver, have been used to receive and process/decode the acquired temperature data. Finally, the temperature information was displayed by a computer via serial data transfer to provide real-time and local data monitoring. In this case, all the equipment at the reception side were powered by the computer USB port.The second step was to add a second TX node in order to evaluate the system scalability. In this scheme, 1.5 W optical power was transmitted through the same fiber link, with attenuation of 1 dB. Thus, we estimated that over 1.2 W optical power was injected into the PPC, resulting in a delivered electrical power of around 360 mW. Finally, we measured the TX node 1 and 2 overall consumed power at the DC/DC converter output, which resulted in approximately 330 mW. Consequently, PTE of around 22% could be achieved with this configuration. The wireless TX 1 transmitted the acquired temperature data from sensor 1 every 100 ms, whereas the TX node 2 simultaneously transmitted the data from sensor 2 every 200 ms. The wireless transmission was performed in the same NLOS environment. At the reception side, the same RX node was employed and the data was displayed by the computer via serial data transfer. The second characterization scenario consisted of employing the designed PoF system to power the IoT RX node, as described in In this scheme, 6 W optical power was transmitted through the same fiber link, with attenuation of 1 dB. Thus, we estimated that over 4.8 W optical power was injected into the PPC. In this case, the DC/DC converter was not required, since the 8.5 V voltage provided by the PoF system properly powers the RX node. Finally, we measured the RX node consumed power at the PPC output, which resulted in approximately 1.4 W due to the PPC 30 % efficiency. Consequently, PTE of around 24% could be achieved with this configuration. One may note that the RX node consumes more power than the optically-powered TX node employed in the first scenario. This difference is due to the Arduino UNO use, which is more complex than the Arduino Pro Mini, demanding more power. Moreover, the Ethernet shield typically consumes 100 mA, as shown in The wireless transmission was performed under the same characteristics of the previous scenario: frequency of 2.4 GHz; data rate of 2 Mbps; output power of 0 dBm; 10 m wireless link at a laboratory environment. At the receiver side, the data acquired from the RX module is sent to Arduino UNO, which processes and sends the data to a web server via Ethernet interface. Basically, we have implemented a data logger, which allows for continuous real-time data logging and monitoring, providing the users comprehensive information about the condition of the equipment being monitored. In addition, data can also be recorded and stored in a database for online or even offline local or remote monitoring. The implemented database was built on an open-source software tool written on PHP (phpMyAdmin), which enables the administration of MySQL database servers over the Web . Figure In this Section, we present the methodology used to evaluate the temperature sensor performance, by means of a comparison between the designed PoF-based approach and conventional industrial sensing system, with the purpose of evaluating our approach feasibility and potential. In this context, a PLC have been employed, which is basically a computer-based device designed to control many types of industrial pieces of equipment and automated systems. Generally, PLCs are composed of five main blocks, including rack assembly, power supply, programming device, input/output section, and central processing unit (CPU) . The comWe have employed a didactic plant that emulates an industrial oven, where the temperature sensor was placed. The oven temperature is controlled by a proportional-integral-derivative (PID) controller embedded in the PLC, as presented in In this scheme, the temperature sensor (LM35) is connected to the oven as an additional sensor which is not used to close the control loop. Aiming to efficiently compare the data acquired directly from the PLC analog input to the data acquired from the optically-powered sensor node, we have employed a temperature sensor with the same characteristics of the temperature sensor employed in the closed-loop. However, no digital filters were enabled in this case. In addition, the control system response is a typical under-damped second order response, which has been used in all temperature measurements. Therefore, the proposed PoF-based IIoT system works in addition to the conventional industrial system, collecting field data which can be used in big data and analytics solutions to improve maintenance, safety and productivity.This section presents the experimental results and discussion regarding the temperature sensor performance comparison between the designed PoF-based approach and industrial PLC. The first analysis consisted in comparing the acquired temperature data from the optically-powered TX node 1 and conventional industrial PLC, representing the first implemented scenario. Although the same temperature sensor was connected to the PLC and Arduino analog inputs, the devices were powered by different DC supplies. The PLC is powered by an independent 24 VDC supply, whereas Arduino and the temperature sensor were powered by the PoF system. In this case, the common ground pin of all devices were connected in order to balance the voltages and provide the same reference frame. Nevertheless, there may be a difference in received voltage at the analog input of the devices at a certain time and at a certain temperature in the oven. Another source of interference that can generate a potential difference between the voltages received by the analog inputs of the devices is caused by the Seebeck effect, which occurs when two junctions of different conductors or semiconductors are subjected to different temperatures .The next comparison consisted in performing the temperature measurements of two optically-powered TX sensor nodes transmitting simultaneously. In this case, we have employed two separate industrial ovens, where two different temperature sensors were placed (sensors 1 and 2). In addition, two independent PLCs were employed in order to enable the simultaneous data acquisition. The TX node 2 and industrial PLC temperature measurements as a function of time are presented in Regarding the second implemented scenario, the last comparison consisted in performing the temperature comparison considering the acquired temperature data when the TX node was powered by the DC supply and the designed PoF system was responsible for powering only the RX node. We have successfully proposed, implemented and characterized a PoF-based sensing system composed of optically-powered wireless sensor nodes aiming at IIoT scenarios. The proposed PoF system consisted of a 975-nm HPLD, a 100 m MMF link, a 30% efficiency PPC, and a step-down DC/DC converter. In order to evaluate the PoF system applicability for powering IoT wireless nodes, we have carried out a voltage stability analysis and compared the obtained results with a conventional DC supply. Our PoF system was able to deliver stable output voltage, with or without the DC/DC converter, at 5 V and 8.5 V, respectively.Furthermore, we have implemented two possible IIoT scenarios to demonstrate the flexibility and potential of the PoF technology for powering sensing systems. The first implemented scenarios consisted in powering multiple IoT wireless sensor nodes, composed of temperature sensors, control boards and wireless TXs. The temperature data was sent over a 10 m wireless link to an RX node, responsible for receiving, processing, and displaying the data by means of a serial interface. In this scheme, over 0.6 W optical power was transmitted and approximately 152 mW electrical power was delivered to power one TX node, resulting in a PTE of around 23%. On the other hand, when powering two TX nodes, 1.5 W optical power was transmitted, 330 mW electrical power was delivered, and PTE of around 22% was achieved.The second implemented scenario consisted in optically-powering the RX node. In this experiment, the TX node was powered by a DC supply for tests purposes and the DC/DC converter was not required. The optically-powered RX node was capable of processing and sending the temperature data to a database stored in the cloud via Ethernet interface. In this scheme, 6 W optical power was transmitted and over 1.4 W electrical power was delivered, resulting in PTE of 24%. The results demonstrated that the PoF system is capable of providing the required power for less and more complex IoT wireless nodes, enabling an intricate WSN.In order to evaluate the designed PoF-based sensing system performance, we have carried out an investigation based on a comparison between the proposed approach and a conventional industrial PLC. The comparison consisted in varying the temperature in an industrial oven and analyzing the data acquired from the optically-powered nodes and the PLC. We have considered the two implemented scenarios, in which the TX nodes and RX node were powered by the PoF system, respectively. The PoF- and PLC-based approaches presented similar temperature measurements over the time for both scenarios. In other words, the proposed PoF-based approach presented excellent performance compared to the industrial PLC, demonstrating that optically-powered IoT wireless nodes could be implemented in a realistic industrial environment.Future works regard the implementation of more IoT wireless sensor nodes, envisaging a more complex and dense WSN installed in a real industrial environment, as well as improving and enhancing the PoF system efficiency and transmission distance. Further experimental evaluations concern the implementation of a different architecture based on media converters aiming to transmit data between optically-powered IoT nodes over an optical fiber link, as well as including a performance investigation and comparison with the wireless link.


To investigate post-traumatic stress disorder (PTSD), perceived professional benefits and post-traumatic growth (PTG) status among Chinese nurses in the context of the COVID-19 pandemic and to compare the differences between nurses working inside and outside Hubei.via WeChat. Nurses who worked at the hospital during the COVID-19 pandemic were the research subjects.From February 18 to February 25, 2020, the authors constructed the questionnaire using the Questionnaire Star platform, and convenience sampling was used to distribute the questionnaire t-test results showed that the difference in perceived professional benefits scores between the two groups was not statistically significant, and the differences in PTG scores and PTSD scores between the two groups were statistically significant. The chi-square test indicated that the difference in PTSD prevalence between nurses working outside and inside Hubei Province was statistically significant and that the PTSD prevalence of nurses working outside Hubei Province was higher than that of nurses working inside Hubei Province. One-way ANOVA and independent sample t-test results showed that age, job title, job assignment, length of service, average monthly income, number of night shifts per month, number of children, and the Nurses’ Perceived Professional Benefits Scale (NPPBS) and post-traumatic growth inventory (PTGI) scores influenced the prevalence of PTSD. Logistic regression showed that the occurrence of PTSD was associated with average monthly income, length of service, number of children, and the PTGI and NPPBS scores.A total of 3,419 questionnaires were received, of which 2,860 nurses were working outside Hubei Province and 559 nurses were working inside Hubei Province. Both groups were exposed to COVID-19. The results indicated that gender, job title, department, average monthly income, the number of night shifts per month, hospital classification, specialization, and previous experience with assisting during disasters were statistically significant. The During the COVID-19 pandemic, nurses working outside Hubei Province reported greater PTSD than those working inside Hubei Province. The occurrence of PTSD was related to average monthly income, length of service, number of children, and the PTGI and NPPBS scores. In recent years, the incidence of infectious disease has increased as new pathogens have been discovered, collectively creating enormous challenges for society regarding disease control . InfectiPublic health emergencies require coordination among different personnel at a large scale, and participants, particularly medical personnel, are subjected to high stress levels that could easily lead to psychological disorders. A survey during the SARS pandemic suggested that the high level of stress, anxiety and depression faced by medical personnel may have caused a long-term psychological impact . The latPost-traumatic growth (PTG) and post-traumatic stress disorder (PTSD) are the most common post-traumatic psychological reactions. PTSD refers to a group of characteristic and persistent symptoms that appear after an individual experiences an unusual threat or traumatic stress event . CompareIn December 2019, COVID-19 emerged and spread in Hubei Province, China . To copeN = Zα2 × p × q/(d2), where α  is the probability of a type I error, p × q is the maximum possible estimate of variance, and d  is the permissible error  to 5 (very satisfied). The total score ranges from 33 to 165, and the higher the score is, the higher the perceived professional benefits. The Cronbach’s α for the questionnaire was 0.958, and the Cronbach’s α of each dimension varied between 0.821 and 0.893.The Nurses’ Perceived Professional Benefits Scale (NPPBS) was derived from the Professional Benefit Scale prepared by Cronbach’s α was 0.874.The post-traumatic growth inventory (PTGI) was derived from that prepared by Cronbach’s α is 0.94  was derived from the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders published by the US PTSD Research Center and is used to assess individuals’ experience of psychological trauma under non-state-of-war conditions. The scale consists of 17 items to evaluate three primary symptoms of PTSD: repeated experience, emotional numbing, and hypervigilance. The PLC-C is frequently used for PTSD diagnosis, intervention, and treatment effect evaluation; it is known for its credibility and efficacy and is one of the most widely applied tools. Each scale item is measured by a score ranging from 1 to 5, with a total possible score ranging from 17 to 85. Several studies use a score of 38 as the threshold value, which effectively and efficiently differentiates PTSD and non-PTSD patients; hence, it can be used to evaluate and identify PTSD . A thres is 0.94 .The study adhered to all ethical principles for the acceptable research conduct with humans outlined by the Declaration of Helsinki. This study was approved by the Ethics Committee of the First People’s Hospital of Zunyi (2020-048), and online informed consent was provided by all survey participants prior to their enrollment. Participants could terminate the survey at any time. The survey was anonymous, and confidentiality of information was assured.via the chi-square test. Quantitative data were tested using the t-test, and the variables that remained statistically significant in univariate tests were subsequently used as inputs in logistic regression analysis. A stepwise method was applied to select the optimal model. Statistical significance was set at P < 0.05.We analyzed the data using SPSS18.0. We conducted preliminary analyses before formal statistical analyses to avoid common method bias. Categorical data were described by instance, and percentages were tested We conducted Harman’s single-factor test with all the measurement items, and the results showed that the primary factor component was approximately 18.90%, which proved that there was no common method bias in our study.P < 0.05)  nurses who were involved in managing COVID-19 within Hubei Province and 2,860 (83.65%) uninvolved nurses outside Hubei Province. The chi-square test was used to compare the differences in demographics between the two groups of nurses, and the results showed that sex, job title, department, average monthly income, the number of night shifts per month, hospital classification, specialization, and previous experience with disaster assistance were statistically significant ( < 0.05) .t-test analysis indicated that the difference in perceived professional benefits between nurses working outside Hubei Province and nurses working inside Hubei Province was not statistically significant , but the difference in the PTG scores between the two groups was statistically significant . The difference in the PTSD total scores between the two groups was statistically significant  (The results of the independent sample = 0.001) .2 = 10.721, P < 0.001), with 55.8% of nurses working outside Hubei Province having PTSD and 48.3% of nurses working inside Hubei Province having PTSD. The total PTSD prevalence rate among the survey respondents was 54.6% . Judging from the values of the results, however, the perceived professional benefits of nurses working inside Hubei Province were higher than those of nurses working outside Hubei Province (140.1 ± 21.3 vs. 138.6 ± 20.7). Few studies have investigated the effect of the COVID-19 pandemic on nurses’ perceived professional benefits, but our study showed that Chinese nurses’ perceived professional benefits improved during the COVID-19 pandemic.Because of the political context, nurses who embarked to assist Hubei perceived their actions with a sense of obligation and responsibility, and their work caused feelings of honor and pride. In the past, nurses had a low social status ; howeverP < 0.05), which implied that nurses inside Hubei Province attained more personal growth throughout their experiences in disease management and containment due to their proactive response. Our results were consistent with several studies that reported that nurses experienced PTG (Our results showed that nurses working inside Hubei Province had higher PTG scores than nurses working outside Hubei Province (nced PTG . Howevernced PTG . Howevernced PTG . TherefoP < 0.05). Length of service and NPPBS score controlled the prevalence of PTSD (OR < 1). In contrast to previous studies, our research did not identify heterogeneity between the sexes, which might be due to a lack of male nurses in the sample. The preceding factors directly determined an individual’s work experience and knowledge of disease, which was consistent with international research outcomes (Previous studies have identified multiple sources of PTSD, such as age, sex, job title, social support, exposure intensity and psychological factors . Occupatoutcomes . In geneoutcomes .In this study, the data were collected within the Chinese context on a large sample of nurses. The prevalence of PTSD was compared between nurses working inside and outside Hubei Province. Moreover, we analyzed factors associated with PTSD. However, this study had several limitations. First, this was a cross-sectional study. Second, our survey subjects were Chinese nurses, whose cultural context may cause their responses to differ from nurses who work in other countries. The results of this survey represented only the situation in China. In addition, the possibilities that our survey participants were involved in COVID-19 nursing at different time periods, their levels of exposure were different, and the fact that our survey was self-reported could increase the heterogeneity of the research. Lastly, we did not follow-up with participants, and we were unable to verify the relationship between exposure time and the PTSD prevalence rate.The pandemic has caused a global disaster, and mainly nurses are fighting the pandemic. Healthcare managers and nursing supervisors should pay attention to their mental health while avoiding influence by nurses’ mental health disorders. In particular, psychological trauma is caused by long-term and continuous work. Increasing the number of nurses, reducing shifts, improving remuneration, and conducting psychological interventions are necessary and critical methods to prevent the loss of nurses, burnout and psychological trauma.Our research found that nurses working outside the Hubei Province reported greater rates of PTSD than those working inside the Hubei Province. The occurrence of PTSD was related to PTG, professional benefits, monthly average income, job title, age, and the number of children. Nevertheless, for future pandemic control and management, efforts should be made to administer psychological intervention to medical personnel to improve their coping abilities.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of the First People’s Hospital of Zunyi. The patients/participants provided their written informed consent to participate in this study.All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Obesity has become a serious problem threatening the health of children and adolescents, and China's one-child policy has affected family structure and parenting practice, which may result in several adverse health outcomes. The present study aims to investigate the association between single-child status and the risk of abdominal obesity in Chinese adolescents and also to compare the differences in the risk of unideal energy-related behaviors.Methods: Data were obtained from a school-based cross-sectional survey conducted in seven provinces of China, in 2012. A total of 31,291 students aged 7–17 years were recruited in this study. Anthropometric measurements were conducted to assess height and waist circumference, and questionnaires were used to obtain information of single-child status, parental educational attainment, parental weight status, and offspring energy-related behaviors. Multivariate logistic regression models were used to estimate the odds ratio (OR) and 95% confidence intervals (95% CI) of single-child status and odds of childhood abdominal obesity and energy-related behaviors.Results: The prevalence of abdominal obesity was 18.2% in single children, which was higher than that of non-single children (13.7%). The prevalence was also higher in single children in different sex and residence subgroups. Logistic regression models showed that single children had 1.33 times  higher odds of abdominal obesity compared to non-single children. Single children had 1.08 times higher odds of physical inactivity , 1.13 times higher odds of excessive sugar-sweetened beverages (SSBs) consumption , and 1.08 times more likely to eat out . Those associations were more remarkable in single girls.Conclusion: Being a single child may be associated with a higher odds of childhood abdominal obesity and unhealthy energy-related behaviors. Future interventions and strategies to prevent abdominal obesity should focus on this high-risk population. Obesity among children and adolescents is one of the emerging public health issues around the world. It is estimated that the global age-standardized prevalence of obesity has increased from 0.9% 0.5–1.3%) in 1975 to 7.8% 6.7–9.1%) in 2016 among boys and from 0.7% 0.4–1.2%) to 5.6% (4.8–6.5%) among girls . A more detailed description of the study design and conduct can be assessed elsewhere . Brieflyn = 2,822), residence area (n = 703), height , weight , offspring energy-related behaviors , parental weight status , and parental educational attainment .In this study, waist circumference is not a compulsive item that should be measured in everyone. It was required that at least half of the students in each selected class have their waist circumference measured. Finally, a total of 43,132 students aged 7–17 years who had data of waist circumference were recorded in the survey, of which 31,291 remained in the analysis sample for the present study, after excluding participants who did not have valid data on single-child status . Waist circumference (WC) was measured with an accuracy of 0.1 cm using a non-elastic tape at the end of a natural breath at the midpoint between the top of the iliac crest and the lower margin of the last palpable rib. Waist-to-height ratio (WHtR) was calculated as WC divided by the height, and a cut-off value of 0.5 was used to define abdominal obesity .Single-child status information was obtained through the parental self-administrated questionnaire by asking “how many children do you have in your family?” If the parents answered that there was only one child in the family, children were put into the “single children” group, and others were put into the “non-single children” group.Information on the energy-related behaviors in childhood was obtained from children's self-administrated questionnaire, and all participating students completed the questionnaires during school hours, under the instruction of trained investigators or teachers to ensure consistency across all sites. Children were asked about daily behavior habits, including two items for energy expenditure behaviors [moderate to vigorous physical activity (MVPA) and screen time] and three items for energy intake behaviors .For energy expenditure behaviors, children were asked to answer by themselves the questions of physical activity and screen time (hours and minutes).Information on child's physical activity was recorded by the International Physical Activity Questionnaire-Short Form (IPAQ-SF) , which hScreen time was asked by the following question: “Over the past 7 days, how much time did you spend on watching TV or playing computer or video games on average?” Students reported the duration (hours and minutes) of watching TV or playing computer or video games per day, and the prolonged screen time was defined as ≥2 h/day.For energy intake behaviors, all participants were asked the frequency (days) of SSBs consumption, and the frequency (days) of fast food consumption and eating out. The questions were as follows: “How many days, over the past 7 days, have you drunk sugar-sweetened beverages?,” “How many days, over the past 7 days, have you eaten fast food?,” and “How many days, over the past 7 days, have you eaten out?” Excessive SSBs consumption was defined as >3 days/week; excessive fast food consumption was defined as ≥1 day/week, and eating out was defined as ≥1 day/week.2 were used to define parental overweight and obesity, respectively, according to the criteria recommended by the Working Group on Obesity in China (WGOC) for Chinese adults , normal , and high . Family socioeconomic status was assessed by the parental highest educational attainment and classified into four groups: (1) junior high school or below, (2) senior high school, (3) junior college, and (4) college or above. Paternal and maternal self-reported height (in centimeters) and weight (in kilograms) were collected from parental questionnaires and used to calculate BMI. BMI of 24 and 28 kg/me adults , and par2) tests or independent-sample Student's t-tests were performed appropriately to examine the difference in categorical or continuous variates between single children and non-single children. Multivariate logistic regression analyses were performed to estimate the odds ratio (OR) and 95% confidence intervals (95% CI) of abdominal obesity and unideal energy-related behaviors in the single children group compared to the non-single children group. Potential confounders were adjusted in the logistic regression models, with sex, age, and residence adjusted in Model 2 and additional birth weight, parental educational attainment, and parental weight status adjusted in Model 3. All statistical analyses were conducted using SPSS software version 20.0 , and a two-sided P ≤ 0.05 was considered as statistically significant.Descriptive statistics were calculated for all variables. Mean and standard deviation (SD) were presented for continuous variables, and frequency and percentage were reported for categorical variables. Chi-square  of the participants were boys and 58.4%  lived in urban areas. The proportion of single children was higher in boys and urban participants (P < 0.001). Additionally, compared to non-single children, single children were identified with higher proportion of excessive SSBs consumption (12.6 vs. 11.2%), excessive fast food consumption (9.4 vs. 8.2%), and eating out ≥1 day/week (51.1 vs. 45.2%), but lower proportion of physical inactivity (32.2 vs. 34.3%) and prolonged screen time (21.0 vs. 26.2%).The descriptive characteristics of the study participants by single-child status are presented in P < 0.001) higher odds of abdominal obesity compared with non-single children. Besides, single-child status was associated with higher odds of physical inactivity , excessive SSBs consumption , and eating out  after adjusted for potential covariates.The associations between single-child status and the odds of abdominal obesity and energy-related behaviors were assessed using multivariate logistic regression models. In total participants , single When further analyzed by sex and residence , similarIn this national representative cross-sectional study, we investigated the association between single-child status and pediatric abdominal obesity and energy-related behaviors. The results suggested that single children had higher prevalence and increased odds of abdominal obesity and also higher odds of excessive SSBs consumption and eating out, compared with those with siblings.To our knowledge, there were several studies investigating the relationship between single-child status and the risk of obesity. Yang  firstly Although previous studies had demonstrated the association between single-child status and childhood obesity, the potential mechanism remains unclear. In this study, we found that single children tend to do less moderate to vigorous physical activity, and similar results were also found in previous studies . For sinDistinctive from other countries around the world, the single-child phenomenon in China was a result of the one-child policy, which was introduced by the Chinese government and conducted as a social or political issue, rather than a parental decision. During the past half-century, the Chinese population control policy has experienced a set of adjustments and modifications, from allowing a second child in rural areas in the late 1980's and in couples who are both single-child in 2000  and finaFor many developed countries, the population is a low-growth type, with a slow or even negative growth for a long time, and the problem of population aging has appeared. Most of these countries have adopted policies to encourage childbirth to varying degrees, such as Japan, France, and Norway. However, there are still some developing countries that implemented the policy of family planning, such as India. In India, efforts have been made over the years by the government to create a favorable policy environment for family planning, such as promoting contraceptive methods, but the policy is suggestive, which was different from the one-child policy in China. China's one-child policy has become history with the promulgation of the new policy to encourage the birth, but its impact on the health of this generation of children and adolescents still deserves attention, and it also provides a case study for other countries in the world.Our findings have some important implications for public health policies. First, single children in China may be an important target population of obesity intervention in the future, and health education measurements at family and community levels should guide their healthy lifestyles as early as possible. Second, our results will help to predict the future obesity trends in China. With the rapid economic development, urbanization, and nutrition transition, children will continue to be exposed to an increasing obesity environment. Therefore, it is important to assess the impact of the large population of single children on the trend of obesity in China. In addition, although the one-child policy in China has become history, the size of single children is likely to remain large for a long time due to the rapid changes in the demographic structure and the legacy of the 30-year one-child policy. The policy can control the rapid rise of population, improve the overall national quality of population using limited resources, and promote social equality and equity. However, such policy may also cause a series of health problems, such as cardiovascular and metabolic diseases, and psychological problems, so the results in our study suggest such policy may provide a case for other countries, especially in developing countries with rapid population growth and population control measures.There were several limitations in this study. At first, this study was based on a cross-sectional survey, which prevented us from making inferences about the causal relationship between single-child status and pediatric abdominal obesity. Secondly, information about offspring energy-related behaviors was obtained from simple self-administered questionnaires, rather than validated scale or objective methods, such as a food frequency questionnaire (FFQ)  or AHEI In conclusion, this study demonstrated that single children had higher prevalence of and increased risk of pediatric abdominal obesity. In addition, we also found that single children tend to do less physical activity and consume more SSBs and fast food, especially for single daughters. Future targeted interventions and strategies to combat obesity should be focused more on this high-risk population. Further studies based on longitudinal data are needed to understand the mechanisms and to develop related intervention programs.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Medical Ethical Committee of Peking University. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.DG and YL performed the data analysis. DG, ZY, YM, MC, YD, and ZZ interpreted, wrote, and finalized the manuscript. JM participated in reviewing and revising of the manuscript. All authors contributed in the conception and design of this study, read, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.