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While measures to detect psychophysical olfactory ability are a crucial part of clinicians' assessment of potential olfactory loss, it gives no indication of how olfaction is experienced by the patient and these different aspects often deviate substantially. To ensure quality and reproducibility of subjectively reported olfactory experience and significance, the Importance of Olfaction Questionnaire (IO-Q) was introduced around a decade ago, and while initial validations have produced promising results, important aspects remain nearly unexamined. For example, the test-retest reliability has rarely been examined and the difference of online versus pen-and-paper administration remains unexplored. Here, we translated IO-Q to Danish and examined its validity, test-retest reliability and mode of administration. A cohort of 179 younger, Danish participants with a high level of English proficiency took the test twice with varying time in-between. The first test was taken digitally and in English, while the second was taken using pen-and-paper and in Danish. The distribution of scores and the relationship between the IO-Q and subscale scores were nearly identical between tests, indicating little to no influence of language/test modality in the sampled population. The internal consistency was comparable to previously published results. Likewise, an acceptable test-retest reliability was observed for the full IO-Q and slightly lower for subscales. No significant effect of time was found across several weeks. In conclusion, the IO-Q performed satisfactorily in all examinations and could therefore serve as a valuable clinical measure of subjective olfactory experience, and its Danish translation shows highly similar characteristics to the original, English version.

BackgroundTreatment of nerve injuries proves to be a worldwide clinical challenge. Acellular nerve allografts are suggested to be a promising alternative for bridging a nerve gap to the current gold standard, an autologous nerve graft.ObjectiveTo systematically review the efficacy of the acellular nerve allograft, its difference from the gold standard (the nerve autograft) and to discuss its possible indications.Material and methodsPubMed, Embase and Web of Science were systematically searched until the 4th of January 2022. Original peer reviewed paper that presented 1) distinctive data; 2) a clear comparison between not immunologically processed acellular allografts and autologous nerve transfers; 3) was performed in laboratory animals of all species and sex. Meta analyses and subgroup analyses (for graft length and species) were conducted for muscle weight, sciatic function index, ankle angle, nerve conduction velocity, axon count diameter, tetanic contraction and amplitude using a Random effects model. Subgroup analyses were conducted on graft length and species.ResultsFifty articles were included in this review and all were included in the meta-analyses. An acellular allograft resulted in a significantly lower muscle weight, sciatic function index, ankle angle, nerve conduction velocity, axon count and smaller diameter, tetanic contraction compared to an autologous nerve graft. No difference was found in amplitude between acellular allografts and autologous nerve transfers. Post hoc subgroup analyses of graft length showed a significant reduced muscle weight in long grafts versus small and medium length grafts. All included studies showed a large variance in methodological design.ConclusionOur review shows that the included studies, investigating the use of acellular allografts, showed a large variance in methodological design and are as a consequence difficult to compare. Nevertheless, our results indicate that treating a nerve gap with an allograft results in an inferior nerve recovery compared to an autograft in seven out of eight outcomes assessed in experimental animals. In addition, based on our preliminary post hoc subgroup analyses we suggest that when an allograft is being used an allograft in short and medium (0-1cm, > 1-2cm) nerve gaps is preferred over an allograft in long (> 2cm) nerve gaps.

The mechanical properties of deep rock masses are significantly influenced by temperature and other factors. The effect of temperature on the strength of deep rock masses will pose a serious challenge to deep resource exploitation and engineering construction. In this paper, the thermal-mechanical coupling calculation model is established by particle flow code (PFC2D) to study the uniaxial compression response of rock masses with microcracks after temperature load. The strength of failure, microcracks, and strain was analyzed. The results show that: (i) When the soft rock thickness ratio Hs/H < 0.5, the displacement caused by the applied temperature is concentrated at the structural plane, and the contact force is concentrated at the end of the initial microcrack. When Hs/H ≥ 0.5, the displacement caused by the applied temperature is concentrated on both sides of the initial microcrack, and the contact force is concentrated in the hard rock area. (ii) The number of microcracks decreases with the increase of soft rock thickness under different working conditions. When the soft rock thickness ratio Hs/H < 0.5, the relationship curve between the number of microcracks and the vertical strain shows two stages of change. When Hs/H ≥ 0.5, the relationship curve between the number of cracks and the vertical strain changes shows three stages of change. (iii) When the soft rock thickness ratio Hs/H < 0.5, the failure strength decreases with the increase of soft rock thickness ratio at T = 100°C and 200°C. When T = 300°C and 400°C, the failure strength decreased first and then increased. When Hs/H ≥ 0.5, the failure strength increases with the increase of soft rock thickness at T = 200°C, 300°C, and 400°C. At T = 100°C, the failure strength decreases with the increase of soft rock thickness.

Researchers often claim that sibling analysis can be used to separate causal genetic effects from the assortment of biases that contaminate most downstream genetic studies (e.g. polygenic score predictors). Indeed, typical results from sibling analysis show large (>50%) attenuations in the associations between polygenic scores and phenotypes compared to non-sibling analysis, consistent with researchers' expectations about bias reduction. This paper explores these expectations by using family (quad) data and simulations that include indirect genetic effect processes and evaluates the ability of sibling analysis to uncover direct genetic effects of polygenic scores. We find that sibling analysis, in general, fail to uncover direct genetic effects; indeed, these models have both upward and downward biases that are difficult to sign in typical data. When genetic nurture effects exist, sibling analysis creates "measurement error" that attenuates associations between polygenic scores and phenotypes. As the correlation between direct and indirect effect changes, this bias can increase or decrease. Our findings suggest that interpreting results from sibling analysis aimed at uncovering direct genetic effects should be treated with caution.

Atypical visual attention in individuals with autism spectrum disorders (ASD) has been utilised as a unique diagnosis criterion in previous research. This paper presents a novel approach to the automatic and quantitative screening of ASD as well as symptom severity prediction in preschool children. We develop a novel computational pipeline that extracts learned features from a dynamic visual stimulus to classify ASD children and predict the level of ASD-related symptoms. Experimental results demonstrate promising performance that is superior to using handcrafted features and machine learning algorithms, in terms of evaluation metrics used in diagnostic tests. Using a leave-one-out cross-validation approach, we obtained an accuracy of 94.59%, a sensitivity of 100%, a specificity of 76.47% and an area under the receiver operating characteristic curve (AUC) of 96% for ASD classification. In addition, we obtained an accuracy of 94.74%, a sensitivity of 87.50%, a specificity of 100% and an AUC of 99% for ASD symptom severity prediction.

BackgroundLatent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry.MethodsComprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods.ResultsOf 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217-7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes.ConclusionsThis review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.

Dialysis is a replacement therapy for patients with End-Stage Renal Disease (ESRD). Patients on dialysis are at high risk of acquiring hepatitis C virus (HCV), which has become a leading cause of morbidity and mortality in this population. There is a wide range of prevalence of HCV in dialysis populations around the world. It is still unknown how prevalent HCV infection is among worldwide dialysis patients (including those undergoing hemodialysis and peritoneal dialysis). A review was conducted to estimate the global epidemiology of hepatitis C in dialysis patients. We searched PubMed, Excerpta Medica Database (Embase), Global Index Medicus and Web of Science until October 2022. A manual search of references from relevant articles was also conducted. Heterogeneity was evaluated by the χ2 test on Cochrane's Q statistic, and the sources of heterogeneity were investigated using subgroup analysis. In order to assess publication bias, funnel plots and Egger tests were conducted, and pooled HCV prevalence estimates were generated using a DerSimonian and Laird meta-analysis model. The study is registered with PROSPERO under CRD42022237789. We included 634 papers involving 392160 participants. The overall HCV case fatality rate was 38.7% (95% CI = 28.9-49). The global prevalence of HCV infection in dialysis population group were 24.3% [95% CI = 22.6-25.9]. As indicated by UNSD region, country, dialysis type, and HCV diagnostic targeted; Eastern Europe had the highest prevalence of 48.6% [95% CI = 35.2-62], Indonesia had 63.6% [95% CI = 42.9-82], hemodialysis patients had 25.5% [95% CI = 23.8-27.3], and anti-HCV were detected in 24.5% [95% CI = 22.8-26.2]. Dialysis patients, particularly those on hemodialysis, have a high prevalence and case fatality rate of HCV infection. Hemodialysis units need to implement strict infection control measures.

Environmental stewardship is a term describing both the philosophy and the actions required to protect, restore, and sustainably use natural resources for the future benefit of the environment and society. In this paper, we review the environmental science literature to map the types of practical actions that are identified as 'environmental stewardship' using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. We specifically mapped: 1) the type of actions and outcomes targeting the natural environment that have been categorized as environmental stewardship, 2) the main actors, and the underlying factors influencing their environmental stewardship actions, and 3) the methods used to mobilize environmental stewardship actions once these factors are known. From the 77 selected articles, we found the term environmental stewardship encompassed a multitude of different actions, undertaken by a range of actors and addressing an array of issues that impact biodiversity on the land and in the water. These stewardship actions were conducted on both privately-owned and publicly managed lands and waterways, and across rural and urban landscapes. Despite many studies identifying characteristics and underlying behavioral factors that predicted actors' participation in stewardship actions, there were few studies formally evaluating interventions to increase stewardship. Our review highlighted the term environmental stewardship is not embraced by all and is viewed by some as being inconsistent with aspects of indigenous worldviews. A better understanding of the concept of environmental stewardship and continued practical research into its practice is fundamental to empowering people to demand and enact environmental stewardship as well as for evaluating the success of their actions.

During the COVID-19 pandemic, concerns were raised that face covering use may elicit risk compensation; a false sense of security resulting in reduced adherence to other protective behaviours such as physical distancing. This systematic review aimed to investigate the effect of face covering use on adherence to other COVID-19 related protective behaviours. Medline, Embase, PsychInfo, EmCare, medRxiv preprints, Research Square and WHO COVID-19 Research Database were searched for all primary research studies published from 1st January 2020 to 17th May 2022 that investigated the effect of face covering use on adherence to other protective behaviours in public settings during the COVID-19 pandemic. Papers were selected and screened in accordance with the PRISMA framework. Backwards and forwards citation searches of included papers were also conducted on 16th September 2022, with eligible papers published between 1st January 2020 and that date being included. A quality appraisal including risk of bias was assessed using the Academy of Nutrition and Dietetics' Quality Criteria Checklist. This review is registered on PROSPERO, number CRD42022331961. 47 papers were included, with quality ranging from low to high. These papers investigated the effects of face covering use and face covering policies on adherence to six categories of behaviour: physical distancing; mobility; face-touching; hand hygiene; close contacts; and generalised protective behaviour. Results reveal no consistent evidence for or against risk compensation, with findings varying according to behaviour and across study types, and therefore confident conclusions cannot be made. Any policy decisions related to face coverings must consider the inconsistencies and caveats in this evidence base.

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

With the increasing uncertainty of urban security, urban resilience construction with risk awareness and bottom-line thinking has become essential for promoting sustainable urban development. This paper measures China's urban resilience development index (CRDI) based on 284 cities in China (except Tibet) using the entropy method from four dimensions: economic, social, environmental, and infrastructure, and analyzes it by combining coupling coordination degree and barrier factor analysis. We find that: (1) At the national level, CRDI and its sub-dimensions show an increasing trend in time, a decreasing spatial layout from coastal to inland, and a "high-high-low-low" clustering feature in space. (2) At the regional level, the CRDI is from high to low in the east, middle, and west order. The sub-dimensions are from high to low in the order of east, middle, and west for economic, social, and infrastructure resilience and from high to low in the order of east, west, and middle for environmental resilience. (3) To coupling coordination degree, the CRDI index coupling coordination is increasing in time trend but is still on the verge of dissonance. (4) Social resilience is the main obstacle factor. In the indicator layer, human resources, innovation, education, security, living, and environmental protection are the areas where CRDI coordinated development is the key to improvement. Based on the above empirical evidence, this paper proposes countermeasures to optimize urban resilience construction from four perspectives: economic, social, environmental, and infrastructure.

BackgroundAcne is a common disease that is associated with scarring and substantial psychosocial burden. The Global Burden of Skin Disease reported that the burden from acne as measured by disability-adjusted life years (DALYs) from 188 countries and specifically that it is greatest in Western Europe, high-income North America and Southern Latin America. This paper aimed to identify risk factors for acne scarring specific to the Ecuadorian population in order to adapt the 4-ASRAT tool accordingly.MethodsThis was an observational prospective study. Participants were recruited to complete a survey that was developed based on the potential risk factors for acne scarring and had facial photographs taken. To determine risk factors and their respective weighting, a logistic regression was performed.ResultsThe study included 404 participants. Results from univariate analyses indicated that being male (OR = 2.76 95%CI [1.72; 4.43]), having severe or very severe acne scarring (OR = 4.28 95%CI [1.24; 14.79]), acne duration over 1 year (OR = 1.71 95%CI [1.12; 2.60]), oily skin (OR = 2.02 95%CI [1.27; 3.22]) and the presence of acne on the neck (OR = 2.26 95%CI [1.30; 3.92]), were all significantly associated with the presence of acne scarring. Male sex (2.56 95%CI [1.58;4.17]), oily skin (1.96 95%CI [1.20;3.20]) and severe or very severe acne (3.75 95%CI [1.05;13.37]) remained significant risk factors for acne scarring in the multivariate analysis.ConclusionBy identifying acne scarring risk factors and applying the tool in everyday dermatology visits, we can reduce the physical and psychological burden that acne scarring causes in the adolescent and adult populations. Further research should be conducted to reassess potential risk factors and complete the adaptation of the tool for the Ecuadorian population, with a larger and more representative study population.

On the edge of causing the sixth big mass extinction event, the development of positive attitudes towards the conservation of intraspecific diversity from early ages is essential to overcome the biodiversity crisis we currently face. However, there is no information available on elementary school students' attitudes toward intraspecific diversity conservation nor is there a framework available to perform such analysis. For this study we designed, implemented, and evaluated an educational activity planned for third graders (8 to 13 years old) to explore the intraspecific diversity of vegetables and promote healthy eating habits. Additionally, a framework was developed to evaluate students' attitudes towards intraspecific diversity conservation and applied to semi-structured interviews conducted with students before and after engaging in the educational activity. In this paper we present a reliable framework, developed aligned with the ABC model of attitudes, based on literature, and adapted to elementary school students' responses, to evaluate students' attitudes toward intraspecific diversity. Our results show that, before the educational activity, most students choose a non-biodiverse option, justifying this choice with the affective component of attitudes: mostly emotional factors but also aesthetic and social/cultural factors. After the educational activity, we observed a significant increase in the frequency of students that choose the biodiverse option and that justified it with the cognitive component of attitudes: mainly with biology and health knowledge factors, but also with economic and ethical knowledge factors. Our findings support the positive impact of educational activities that explore vegetable varieties on students' attitudes toward intraspecific diversity conservation. This activity may also be used to foster education for sustainability and address socioscientific issues aligned with diverse sustainable development goals.

IntroductionThe COVID-19 pandemic disproportionately affected older adults, particularly those with pre-existing chronic health conditions. To address the health disparity and challenges faced by under-resourced African American older adults in South Los Angeles during this period, we implemented a hybrid (virtual/in-person), pre-post, community-based participatory intervention research project utilizing a faith-based lay health advisor model (COVID-19 Health Ambassador Program (CHAP)). We recruited COVID-19 Health Ambassadors (CHAs) and African American older adults (participants) from faith-based organizations who partook in CHA-led meetings and follow-ups that educated and supported the participants. This paper seeks to evaluate this intervention's implementation using the Consolidated Framework for Implementation Research (CFIR) as a reporting tool with an emphasis on fidelity, challenges, and adaptations based on data collected via stakeholder interviews and surveys.ResultsCHAP was delivered to 152 participants by 19 CHAs from 17 faith-based organizations. CHAs assisted with chronic disease management, resolved medication-related challenges, encouraged COVID-19 vaccination, reduced psychological stress and addressed healthcare avoidance behaviors such as COVID-19 vaccine hesitancy among the participants. Challenges encountered include ensuring participant engagement and retention in the virtual format and addressing technological barriers for CHAs and participants. Adaptations made to better suit the needs of participants included providing communication tools and additional training to CHAs to improve their proficiency in using virtual platforms in addition to adapting scientific/educational materials to suit our participants' diverse cultural and linguistic needs.ConclusionThe community-centered hybrid approach in addition to our partnership with faith-based organizations and their respective COVID-19 health ambassadors proved to be essential in assisting underserved African American older adults manage chronic health conditions and address community-wide health disparities during the COVID-19 pandemic. Adaptability, cultural sensitivity, and teamwork are key to implementing health interventions especially in underserved populations.

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.

Surface dielectric barrier discharge (SDBD) has wide applications in flow control, wastewater treatment, and biomedicine. The dielectric surface of an SDBD actuator is generally attached to the water droplets during applications. Thus far, only a few studies have been conducted on the effects of water covering the dielectric surface on the discharge characteristics of SDBD. Therefore, the effects of water droplets on the discharge of an SDBD actuator based on a repetitive microsecond pulse power supply were investigated in this study. The results show that a filament micro-discharge channel forms between the light and dark regions at the internal edge of the SDBD high-voltage electrode and develops toward the center of the dielectric surface in the region without water droplet coverage. SDBD in the water-covered region was divided into two stages. This paper compares the electrical characteristics of SDBD with and without water droplet, and explores the electric field distortion effect of water droplet endpoints through 3D simulation.Based on the theories of water droplet polarization and gas discharge, the effects of water droplets on plasma development and surface charge accumulation under water-covered condition were analyzed. The water droplet plays a similar role as a "secondary electrode" during the discharge process.

BackgroundThe last three decades have seen a growth in the number of children requiring long-term ventilation. Children with long-term ventilation present with underlying respiratory and neurological conditions that place them at risk of feeding and swallowing difficulties. To date, a scoping review or systematic review investigating the feeding and swallowing outcomes of children with long-term ventilation needs has not been conducted.AimsThis paper describes a protocol for a scoping review of the feeding and swallowing outcomes of children receiving long-term ventilation.MethodsThis scoping review protocol will utilize the Joanna Briggs Institute scoping review methodology guideline. Our review will focus on the feeding and swallowing outcomes of children aged 0 to 18 years with long-term ventilation needs. A full search strategy initially created by the authors and a research librarian was conducted on the PubMed database. Following this, pilot testing took place to determine discrepancies in eligibility criteria. A full search strategy will be conducted across several databases. A data extraction form has been developed by the authors and will be used during the scoping review process.DiscussionThis protocol has been created to provide a rigorous and comprehensive basis for undertaking a scoping review. All necessary steps have been completed in order to commence the scoping review.RegistrationThis scoping review protocol was registered on Open Science Framework on the 26th November 2021 (Registration DOI 10.17605/OSF.IO/NQBPD).

This paper methodically investigates the influence of inclusive income growth on city size, examining it through the dual lenses of "income" and "distribution." The analysis leverages meticulously collected panel data encompassing 276 Chinese cities at the prefecture level and above, spanning the period from 2005 to 2019. Theoretical analysis indicates that the effect of city size expansion on per capita income adheres to a 'U'-shaped trajectory, while its influence on the urban-rural income gap manifests an 'inverted U' pattern. Moreover, the inclusive income growth stemming from city size demonstrates notable heterogeneity across various geographic locations and city hierarchies. The findings reveal that human capital serves as the primary mechanism through which city size influences inclusive income growth. After decomposing the income inclusiveness index, it becomes evident that the expansion of city size exerts a more potent direct driving effect on the income of urban residents. On the one hand, city size expansion directly increases rural residents' income levels by improving labor productivity. On the other hand, it facilitates leapfrog income development by inducing the rural labor force to move to cities.

This paper examines whether nanotechnology projects funded under the European Union (EU) Framework Programmes (FPs) are a possible trigger for path upgrading (i.e., infusion of new technologies in existing traditional sectors) in less-advanced regions. First, the adoption of cluster analysis and a set of key indicators (i.e., technological intensity, scientific excellence, human capital, and research and development expenditure) allowed us to distinguish between 79 more-advanced and 127 less-advanced EU regions. Subsequently, through social network analysis and nonparametric testing we were able to demonstrate how the less-advanced EU regions (average degree centrality: 40.5) play a marginal role compared with the more-advanced ones (average degree centrality: 98.5) in the nanotechnology network created within Horizon 2020-i.e., the EU programming cycle implemented in the 2014-2020 period. Despite this, we observed that a few less-advanced regions (33 out of 127) were able to score higher than the EU median in terms of participation in the targeted nanotechnology network, thus benefiting from relevant knowledge flows potentially leading to re-industrialization processes. The adoption of qualitative comparative analysis allowed us to determine which combinations of key innovation, scientific and socioeconomic factors could facilitate such beneficial interregional interactions and related knowledge exchange in these types of regions (i.e., primarily what we defined as "relative innovativeness," excellence in nanotechnology research and a comparatively high level of gross domestic product per capita). Our empirical results provided some clear policy implications. For instance, the necessity to I) remove the barriers impeding a more balanced participation to promote a widespread renewal of traditional industries in less-advanced regions and II) implement coordinated EU and domestic actions designed to encourage the involvement of the great majority of the less-advanced regions, which remain marginal in the periodically launched FPs.

Brazil counts among the countries the hardest hit by the Covid-19 pandemic. A great deal has been said about the negative role played by President Bolsonaro's denialism, but relatively few studies have attempted to measure precisely what impact it actually had on the pandemic. Our paper conducts econometric estimates based on observational data at municipal level to quantitatively assess the 'Bolsonaro effect' over time from March 2020 to December 2022. To our knowledge, this paper presents the most comprehensive investigation of Bolsonaro's influence in the spread of the pandemic from two angles: considering Covid-19 mortality and two key transmission mitigation channels (social distancing and vaccination); and exploring the full pandemic cycle (2020-2022) and its dynamics over time. Controlling for a rich set of relevant variables, our results find a strong and persistent 'Bolsonaro effect' on the death rate: municipalities that were more pro-Bolsonaro recorded significantly more fatalities. Furthermore, evidence suggests that the president's attitude and decisions negatively influenced the population's behaviour. Firstly, pro-Bolsonaro municipalities presented a lower level of compliance with social distancing measures. Secondly, vaccination was relatively less widespread in places more in favour of the former president. Finally, our analysis points to longer-lasting and damaging repercussions. Regression results are consistent with the hypothesis that the 'Bolsonaro effect' impacted not only on Covid-19 vaccination, but has affected vaccination campaigns in general thereby jeopardizing the historical success of the National Immunization Program in Brazil.

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.

To control the SARS-CoV-2 pandemic, healthcare systems have focused on ramping up their capacity for epidemiological surveillance through viral whole genome sequencing. In this paper, we tested the performance of two protocols of SARS-CoV-2 nucleic acid enrichment, an amplicon enrichment using different versions of the ARTIC primer panel and a hybrid-capture method using KAPA RNA Hypercap. We focused on the challenge of the Omicron variant sequencing, the advantages of automated library preparation and the influence of the bioinformatic analysis in the final consensus sequence. All 94 samples were sequenced using Illumina iSeq 100 and analysed with two bioinformatic pipelines: a custom-made pipeline and an Illumina-owned pipeline. We were unsuccessful in sequencing six samples using the capture enrichment due to low reads. On the other hand, amplicon dropout and mispriming caused the loss of mutation G21987A and the erroneous addition of mutation T15521A respectively using amplicon enrichment. Overall, we found high sequence agreement regardless of method of enrichment, bioinformatic pipeline or the use of automation for library preparation in eight different SARS-CoV-2 variants. Automation and the use of a simple app for bioinformatic analysis can simplify the genotyping process, making it available for more diagnostic facilities and increasing global vigilance.

ObjectiveThis study aimed to assess the correlation between the circulating cell-free mitochondria DNA and inflammation factors in noninfectious disease by meta-analysis of data from eligible studies.Materials and methodsThrough a comprehensive searching of pubmed, embase, web of science, cochrane from establishment of the database to October 31, 2022, studies were selected that investigated the association of circulating cell free mitochondria DNA with inflammatory factors in non-infectious diseases. Studies that met the inclusion criteria and were published in English or Chinese were included. Data of each correlation coefficients were extracted from the paper and 95% confidence intervals were calculated. Sensitivity and heterogeneity tests were carried out for each data.ResultsA total of 660 articles were retrieved and 22 were included in this meta-analysis, including 2600 patients. A fixed effects model was employed to examine ISS and IL-8, others were analyzed using random effects models. The correlation coefficient between mtDNA and ISS score was 0.37 (95%CI = [0.232;0.494]), pConslusionCirculating cell free mtDNA was moderate positively correlated with the expression of inflammatory factors and the degree of trauma.

Singing voice separation on robots faces the problem of interpreting ambiguous auditory signals. The acoustic signal, which the humanoid robot perceives through its onboard microphones, is a mixture of singing voice, music, and noise, with distortion, attenuation, and reverberation. In this paper, we used the 3D Inception-ResUNet structure in the U-shaped encoding and decoding network to improve the utilization of the spatial and spectral information of the spectrogram. Multiobjectives were used to train the model: magnitude consistency loss, phase consistency loss, and magnitude correlation consistency loss. We recorded the singing voice and accompaniment derived from the MIR-1K dataset with NAO robots and synthesized the 10-channel dataset for training the model. The experimental results show that the proposed model trained by multiple objectives reaches an average NSDR of 11.55 dB on the test dataset, which outperforms the comparison model.

PurposeDue to the existence of information opacity, there is a common problem of adverse selection in the process of screening alternative technology start-ups (TSs) and determining investment targets by venture capital institutions, which does not reveal the true value of enterprises and makes the market inefficient. The aim of this paper is to design an evaluation and screening system help venture capital institutions to select the qualified TSs as their investment objective.DesignA research framework of four dimensions that include conception, technical innovation, business model and team structure, was built based on previous studies. Based on the research framework, 15 second-level indicators and 33 third-level indicators were extracted with literature research method. This paper proposes an evaluation model with back propagation neural network (BPNN) optimized by genetic algorithm (GA) to improve the rate of selecting and investing in qualified start-ups.FindingsThe results show that the evaluation accuracy of the evaluation model for qualified and unqualified enterprises can reach 80.33% and 93.67% respectively, which has verified the effectiveness of the model and algorithm.Originality/valueThis paper established an effective evaluation system based on PCA and GA-BPNN to help venture capital institutions preliminarily screen potential technology start-ups, which provides the possibility for venture capital institutions to greatly reduce the screening time and cost, improve the screening efficiency of TSs, and scientifically assess the risk of investee projects or investee enterprises to obtain sustainable and stable excess profits.

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases.

BackgroundAmong the most urgent public health challenges, of the twenty-first century, is obesity. This can be attributed to its relationship with several non-communicable diseases (NCDs), as well as premature mortality. Being overweight or obese is a major concern not only in high-income countries, but also in low-income and middle-income countries, particularly in urban areas. Several studies have highlighted the prevalence of obesity, among Middle Eastern-descent adolescents, studying in Arabic secondary schools, located in Malaysia. Intervention studies, directed at Middle Eastern adolescents in Malaysia, are limited. This paper, describes the protocol, for an integrated health education intervention process. Titled 'Healthy lifestyle', it is a primary prevention process, aimed at curbing obesity and disordered eating, among Middle Eastern secondary school adolescents, aged 13-14 years old, residing in Malaysia.Methods and anticipated resultsA cluster randomized controlled study will be conducted, involving 250 Middle Eastern adolescents, in Arabic schools in Malaysia. The participants will be randomly assigned to the intervention and control groups. While the intervention group participates in six weeks of fortnightly six sessions (45 minutes per session), the control group will carry on with their regular curriculums, and normal physical activity routines. The variables which will be evaluated include anthropometric measurements, knowledge, attitude, daily routines, physical activity, sedentary behaviour, food assessment, eating attitudes test-26, and a structured questionnaire based on the HBM. Data will be collected from the intervention and control groups at baseline, post-intervention, and two months following the intervention. Data analysis will be performed by way of the SPSS Statistics software version 26. The generalized estimating equation (GEE) will be used, to test the effect of the intervention program, with regards to the selected variables (outcomes), between and within-group at baseline, as well as six weeks and two months following intervention, after adjusting for clustering. Outcomes will be assessed at each time point, along with a derived average over all three-time points; thus, ensuring that both the cumulative and overall effects are determined.ConclusionsThis trial will provide useful information for improving the knowledge, attitude, and practices of Middle Eastern adolescents, with regards to body weight status, physical activity level, nutrition status (BMI and dietary intake), and disordered eating. This will go a long way, towards ensuring their adherence to appropriate physical activities, and a healthy diet, to keep non-communicable diseases at bay.Trial registrationThis study is registered at NCT: NCT05694143.

BackgroundCancer diagnosis based on machine learning has become a popular application direction. Support vector machine (SVM), as a classical machine learning algorithm, has been widely used in cancer diagnosis because of its advantages in high-dimensional and small sample data. However, due to the high-dimensional feature space and high feature redundancy of gene expression data, SVM faces the problem of poor classification effect when dealing with such data.MethodsBased on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). The algorithm firstly calculates the information gain values of the features and ranks them in descending order according to the value. Then, ranked features are grouped according to the information index, so that the features in the group are close, and the features outside the group are sparse. Finally, grouped features are searched using grouping PSO and evaluated according to in-group and out-group.ResultsExperimental results show that the average accuracy (ACC) of the SVM on the feature subset selected by the IG-GPSO is 98.50%, which is significantly better than the traditional feature selection algorithm. Compared with KNN, the classification effect of the feature subset selected by the IG-GPSO is still optimal. In addition, the results of multiple comparison tests show that the feature selection effect of the IG-GPSO is significantly better than that of traditional feature selection algorithms.ConclusionThe feature subset selected by IG-GPSO not only has the best classification effect, but also has the least feature scale (FS). More importantly, the IG-GPSO significantly improves the ACC of SVM in cancer diagnostic.

Various interventions have been investigated to improve the uptake of colorectal cancer screening. In this paper, the authors have attempted to provide a pooled estimate of the effect size of the BE interventions running a systematic review based meta-analysis. In this study, all the published literatures between 2000 and 2022 have been reviewed. Searches were performed in PubMed, Scopus and Cochrane databases. The main outcome was the demanding the one of the colorectal cancer screening tests. The quality assessment was done by two people so that each person evaluated the studies separately and independently based on the individual participant data the modified Jadad scale. Pooled effect size (odds ratio) was estimated using random effects model at 95% confidence interval. Galbraith, Forrest and Funnel plots were used in data analysis. Publication bias was also investigated through Egger's test. All the analysis was done in STATA 15. From the initial 1966 records, 38 were included in the final analysis in which 72612 cases and 71493 controls have been studied. About 72% have been conducted in the USA. The heterogeneity of the studies was high based on the variation in OR (I2 = 94.6%, heterogeneity X2 = 670.01 (d.f. = 36), p < 0.01). The random effect pooled odds ratio (POR) of behavioral economics (BE) interventions was calculated as 1.26 (95% CI: 1.26 to 1.43). The bias coefficient is noteworthy (3.15) and statistically significant (p< 0.01). According to the results of this meta-analysis, health policy and decision makers can improve the efficiency and cost effectiveness of policies to control this type of cancer by using various behavioral economics interventions. It's noteworthy that due to the impossibility of categorizing behavioral economics interventions; we could not perform by group analysis.

The question of whether China can become a creative nation has been a topic of much debate in academic circles. The Chinese government has expressed its belief that China can develop a unique form of creativity to move the country from the periphery to the center of the global creative ecosystem. This perspective has led to a series of state-led trials and experiments, including the adoption of cultural and creative industries, creative clusters and cities, and the recent maker movement. This paper utilizes the center-periphery theory to analyze the emergence, development, and evolution of China's maker movement, aiming to revisit the creativity issues in contemporary China. Based on three years of ethnographic research, the paper unpacks the maker movement at three interrelated levels: individual, organizational, and urban. Empirical data indicates that the transformation of China's maker movement is characterized by commodification, formalization, and infrastructuralization processes. The tension between growth and development, and stability and control has turned the once grassroots maker movement into a contested creative hybrid. This paper challenges the conventional view that China is resistant to change and incapable of creativity due to institutional and ideological influences. It demonstrates how an alternative mode of creativity can emerge outside global creative centers and proposes a new perspective on China's potential to become a creative nation.

This paper studies mechanical properties and energy damage evolution of fiber-reinforced cemented sulfur tailings (CSTB) backfill. The effects of fiber length and fiber content on the stress, toughness and failure properties of the CSTB were systematically revealed. In addition, the energy index evolution law was studied, and the energy damage evolution mechanism of CSTB was revealed. The results show that the deformation failure of fiber-reinforced CSTB mainly goes through four stages: initial crack compaction, linear elastic deformation, yield failure and post-peak failure. The peak stress and residual stress of the CSTB firstly increase and then decrease with the increase of fiber content and the addition of fiber can promote the change from brittle failure to ductile failure of the CSTB. Adding appropriate amount of fiber can improve the toughness of CSTB, and the influence degree of fiber length on the toughness index of CSTB is 6mm>12mm>3mm. The total strain energy increases linearly along the variation of fiber content, while the elastic strain energy and dissipated energy increase exponentially at the peak stress point. In the process of CSTB deformation and failure, "gentle-linear growth-slow growth-rapid decline" is for elastic strain energy, while "gentle-slow growth-rapid growth-linear growth" is for dissipation energy. The damage and failure of CSTB mainly experienced four stages: initial damage, slow growth of damage, accelerated damage and damage failure, and the damage evolution curve also showed the changing characteristics of "gentle-slow growth-rapid growth-linear growth". The CSTB without added fiber showed obvious "Y-type" and "linear-type" shear failure characteristics and the phenomenon of shear cracks penetrating the backfill appeared. No big shear crack occur when it is damaged, showing that the fiber addition restrain the crack growth and improve the overall crack resistance of the CSTB. Hydration products are obviously distributed on the surface of the fiber, which indicates that the fiber will be evenly dispersed in the CSTB and form a certain bonding force with the cement-tailings matrix, thus improving the overall mechanical properties of the CSTB.

In many applications, artificial neural networks are best trained for a task by following a curriculum, in which simpler concepts are learned before more complex ones. This curriculum can be hand-crafted by the engineer or optimised like other hyperparameters, by evaluating many curricula. However, this is computationally intensive and the hyperparameters are unlikely to generalise to new datasets. An attractive alternative, demonstrated in influential prior works, is that the network could choose its own curriculum by monitoring its learning. This would be particularly beneficial for continual learning, in which the network must learn from an environment that is changing over time, relevant both to practical applications and in the modelling of human development. In this paper we test the generality of this approach using a proof-of-principle model, training a network on two sequential tasks under static and continual conditions, and investigating both the benefits of a curriculum and the handicap induced by continuous learning. Additionally, we test a variety of prior task-switching metrics, and find that in some cases even in this simple scenario the a network is often unable to choose the optimal curriculum, as the benefits are sometimes only apparent with hindsight, at the end of training. We discuss the implications of the results for network engineering and models of human development.

Economic policy uncertainty has had an important impact on trade and sustainable economic development. Especially in some specific industries, uncertainty has increased dramatically. The extant related literature mainly analyzes the nexus between uncertainty and trade across different industries and focuses less on a specific industry. Using Chinese customs data on HS 8-digit products over the period of 2000-2013, this paper first investigates the impact of both foreign economic policy uncertainty (EPU) and domestic intra-industry trade on China's mechanical and electrical product exports to 23 trading partners and applies pooled OLS regressions to conduct an empirical study. This paper finds that EPU has a significant inhibition effect on mechanical and electrical product exports; conversely, intra-industry trade can both significantly promote exports and alleviate the inhibition effect of EPU. In addition, the export impact of EPU varied with different trade patterns. It can significantly inhibit processing exports, while it has no effect on ordinary exports. The results of this paper indicate that in the context of increasing uncertainty, our findings could have far-reaching policy implications for China to build a new development pattern of domestic and international dual circulation.

In Kenya, approximately one in five girls aged 15-19 years old are pregnant or already a mother. Adolescent girls and young women experience significant mental health vulnerabilities during the pregnancy and postpartum periods, leading to poor antenatal and postnatal care attendance and inferior infant and maternal health outcomes. Pregnant adolescents often experience stigma and disenfranchisement due to their pregnancy status and at the same time lack access to mental health support within health settings, schools, religious institutions, and communities. This paper presents the results of qualitative interviews embedded within the human-centered design (HCD) process used to adapt the Helping Adolescents Thrive (HAT) program for Kenyan peripartum adolescents including young fathers. This qualitative study used two phases. First, a HAT advisory group participated in a series of four workshops to help identify and articulate mental health promotion needs and deepened the team's understanding of youth-centered thinking. Second, qualitative interviews were conducted with 39 pregnant and parenting adolescents to understand their perspectives on mental health prevention and promotion. Pregnant and parenting adolescents articulated different needs including poor support, stigma, and psychological disturbances. Parenting adolescents reported disturbed relationships, managing motherhood, poor health, and social empowerment. Participants highlighted sources of stress including economic challenges, fear of delivery, strained relationships, rejection, and stigma. Participants described psychological disturbances such as feeling stressed, worthless, withdrawn, and suicidal. Coping mechanisms reported by participants included engaging in domestic activities, hobbies, and social networking. Peers, family and spirituality were identified as important sources of support, as well as school integration, livelihoods, support groups and mentorships. Findings from this study can be used to strengthen and adapt HAT program, policy and practice for mental health prevention and promotion for pregnant and parenting adolescents.

BackgroundData on SARS-CoV-2 infection in pregnancy and infancy has accumulated throughout the course of the pandemic, though evidence regarding asymptomatic SARS-CoV-2 infection and adverse birth outcomes are scarce. Limited information is available from countries in sub-Saharan Africa (SSA). The pregnant woman and infant COVID in Africa study (PeriCOVID Africa) is a South-South-North partnership involving hospitals and health centres in five countries: Malawi, Uganda, Mozambique, The Gambia, and Kenya. The study leveraged data from three ongoing prospective cohort studies: Preparing for Group B Streptococcal Vaccines (GBS PREPARE), SARS-CoV-2 infection and COVID-19 in women and their infants in Kampala and Mukono (COMAC) and Pregnancy Care Integrating Translational Science Everywhere (PRECISE). In this paper we describe the seroepidemiology of SARS-CoV-2 infection in pregnant women enrolled in sites in Uganda and Malawi, and the impact of SARS-CoV-2 infection on pregnancy and infant outcomes.OutcomeSeroprevalence of SARS-CoV-2 antibodies in maternal blood, reported as the proportion of seropositive women by study site and wave of COVID-19 within each country.MethodsThe PeriCOVID study was a prospective mother-infant cohort study that recruited pregnant women at any gestation antenatally or on the day of delivery. Maternal and cord blood samples were tested for SARS-CoV-2 antibodies using Wantai and Euroimmune ELISA. In periCOVID Uganda and Malawi nose and throat swabs for SARS-Cov-2 RT-PCR were obtained.ResultsIn total, 1379 women were enrolled, giving birth to 1387 infants. Overall, 63% of pregnant women had a SARS-CoV-2 positive serology. Over subsequent waves (delta and omicron), in the absence of vaccination, seropositivity rose from 20% to over 80%. The placental transfer GMR was 1.7, indicating active placental transfer of anti-spike IgG. There was no association between SARS-CoV-2 antibody positivity and adverse pregnancy or infancy outcomes.

Long short-term memory (LSTM) has been effectively used to represent sequential data in recent years. However, LSTM still struggles with capturing the long-term temporal dependencies. In this paper, we propose an hourglass-shaped LSTM that is able to capture long-term temporal correlations by reducing the feature resolutions without data loss. We have used skip connections in non-adjacent layers to avoid gradient decay. In addition, an attention process is incorporated into skip connections to emphasize the essential spectral features and spectral regions. The proposed LSTM model is applied to speech enhancement and recognition applications. The proposed LSTM model uses no future information, resulting in a causal system suitable for real-time processing. The combined spectral feature sets are used to train the LSTM model for improved performance. Using the proposed model, the ideal ratio mask (IRM) is estimated as a training objective. The experimental evaluations using short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) have demonstrated that the proposed model with robust feature representation obtained higher speech intelligibility and perceptual quality. With the TIMIT, LibriSpeech, and VoiceBank datasets, the proposed model improved STOI by 16.21%, 16.41%, and 18.33% over noisy speech, whereas PESQ is improved by 31.1%, 32.9%, and 32%. In seen and unseen noisy situations, the proposed model outperformed existing deep neural networks (DNNs), including baseline LSTM, feedforward neural network (FDNN), convolutional neural network (CNN), and generative adversarial network (GAN). With the Kaldi toolkit for automated speech recognition (ASR), the proposed model significantly reduced the word error rates (WERs) and reached an average WER of 15.13% in noisy backgrounds.

As an important part of the cultural industry, small and medium-sized online game enterprises undertake the functions of technological innovation, employment absorption and cultural cultivation. However, the lack of credit ability restricts the financial development of such enterprises. To solve the financing problem of online small and medium-sized game enterprises, this paper firstly uses the information of intangible assets to identify their credit ability, and considers that the information of intangible assets is a problem worthy of attention in evaluating credit risk. Secondly, the intangible assets information disclosure index, the revenue sharing contract of credit synergy and the dynamic game mechanism are constructed to study the importance of the intangible assets index and the evolution of the dynamic game. Finally, the empirical study shows that the intangible assets of delisting and special treated online game small and medium-sized enterprises still have value, this type of enterprise and credit suppliers have the behavior of seeking advantages and avoiding disadvantages. Therefore, credit synergy should be constructed and government regulation should be implemented.

Under the influence of development strategies with regard to national fitness and health in China, the interactive development between national fitness and national health is becoming increasingly strong. To explore the coupling and coordination relationship between national fitness and national health, this paper conducts an empirical analysis of the coupling and coordination relationship between national fitness and national health in 11 provinces and cities in Eastern China using the entropy weight method, a coupling coordination model, spatial visualization of the coupling coordination degree and spatial autocorrelation analysis. The research confirms that the comprehensive development level of national fitness and national health in Eastern China shows a steady upward trend, with a lag in national fitness as a whole, and that the growth rate of national fitness development is faster than that of national health development. The coupling coordination degree of the two systems of national fitness and national health in Eastern China shows a slow upward trend, and the coupling coordination degree rises from barely coordinated to primary coordination. There are significant differences in the spatial pattern of coupling coordination: the spatial pattern from north to south generally shows 'low-high-high-low-high-low' characteristics, and the spatial spillover effect of coupling coordination in various regions has not yet appeared. The revised GM(1.1) prediction results indicate that the level and improvement rate of coupling coordination will accelerate significantly in the next 10 years, but the spatial differences will still exist. Finally, suggestions are proposed to optimize the coupling and coordinated development of national fitness and national health based on policy guarantees as well as strengthening and cross-regional cooperation.

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.

IntroductionResearch indicates that sexual and gender minority youth [SGMY] may engage more with information communication technologies [ICTs] more than their non-SGMY counterparts Craig SL et al. 2020. While scholarship generally explores youth's use of ICTs, there are gaps in scholarship that connect SGMY, their ICT engagement and influences to mental health. This scoping review will synthesize the literature that connects these core concepts in order to better understand the influence ITCs have on the mental health of SGMY and to develop a more fulsome understanding of this emerging area of literature.Methods and analysisFollowing the scoping review framework of Arksey and O'Malley, the search will be conducted in the PsycINFO [Ovid interface, 1980-], MEDLINE [Ovid interface, 1948-], CINAHL [EBSCO interface, 1937-], Sociological Abstracts [ProQuest interface, 1952-], Social Services Abstracts [ProQuest interface, 1979-], and Scopus. Descriptive summaries and thematic analysis will summarize the articles that meet the inclusion criteria using an extraction table.Ethics and disseminationThe review outlined in this paper provides an overview of information that exists on the technology use of SGMY, ICTs and the interconnection with mental health. Results will be disseminated through peer reviewed journals and national and international conferences. As information collected for this paper as is retrieved from publicly available sources, ethics approval is not required.

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

Synchronization of the chaotic systems has attracted much attention in recent years due to its vital applications in secured communication systems. In this paper, an implementation and comparative analysis of two different control approaches for synchronization between two identical four-dimensional hyperchaotic systems is presented. The two control approaches are the Adaptive nonlinear controller and the linear optimal quadratic regulator LQR. To demonstrate the effectiveness of each controller, the numerical simulation is presented using Matlab/Simulink and the control law is derived. The performance of the proposed controllers is compared based on four factors; response time, squared error integration, energy applied from the controller, and cost function. To measure the robustness of the control approaches, the performance factors are compared when there is a change in system parameters and a variation in the initial conditions. Then the proposed synchronization methods are implemented on the FPGA platform to demonstrate the utilized resources on Field Programmable Gate Array (FPGA) hardware platform and the operation speed. Finally, to generalize the results of the comparison, the study is implemented for the synchronization of another secured communication system consisting of two identical three-dimensional chaotic. The experimental results show that the LQR method is more effective than the Adaptive controller based on the performance factors we propose. Moreover, the LQR is much simpler to implement on hardware and requires fewer resources on the FPGA.

International industrial transfer (IIT) has spawned changes in the deep-seated structural power of value-added. We creatively construct an IIT index which includes both scale and direction, and constructs the structural power (SP) of value-added based on added value. Furthermore, based on 15 RECP countries from 1995 to 2018, this study uses a two-fixed-effect regression model to investigate the IIT on the structural power of value-added of RCEP countries. The results show that: (1) IIT can significantly promote the structural power of value-added of RCEP countries, and the benchmark regression conclusion is still valid after a series of robustness tests; (2) Heterogeneity analysis shows that IIT has a more significant promoting effect on the structural power of value-added in developed countries than in developing countries and a more significant promoting effect on the structural power of value-added inward than on the structural power of value-added outward; (3) Intermediary mechanism test shows that IIT mainly affects the structural power of value-added through trade openness and foreign direct investment. The conclusions of this paper provide useful enlightenment for enhancing the structural power of manufacturing value-added in RCEP countries in the context of global value chain division.

This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.

A multiple-input-multiple-output (MIMO) antenna system with low-profile and small element spacing characteristics is presented in this paper. This antenna contains multiple elements arranged in both E-plane and H-plane configurations. The original strong coupling between the MIMO elements can be suppressed by exciting orthogonal operating modes. To achieve this, a half-wavelength microstrip line and a quarter-wavelength grounded stub are utilized to decouple the H- and E-plane MIMO arrays. A 2 × 2 antenna prototype is fabricated and measured to demonstrate the decoupling concept's feasibility. The measured impedance bandwidth is from 4.78 to 4.81 GHz. Across this band, the isolation is better than 15 dB with extremely small edge-to-edge distances of 0.032λ and 0.026λ in the E- and H-plane, respectively. Featuring the simple decoupling structure, small element spacing, and the capability of extending to a large-scale 2 × N array, the proposed antenna can be used for 5G Internet of Things (IoT) applications operating at the N79 frequency band.

In environmentally sensitive areas, especially the arid and semi-arid regions, the greening stability process and its influencing factors can directly affect the sustainable development of the ecological environment. In this study, multi-source remote sensing data such as land use/cover data, MODIS NDVI, and soil moisture, methods such as stability index, vegetation quantitative remote sensing, and Geodetector were employed to analyze the sustainability of the greening process in the Mu Us Sandy in 2000-2020, which were viewed from three aspects: changes in stability of land use types and function, soil moisture change and influencing factors on greening stability. The results showed that, (1) From the stability of land use types, continuous stable ecological land accounted for more than 50%, showing that decreased from northwest toward southeast. (2) From the functional stability, NDVI showed a fluctuated growth (0.035/a), with an increasing distribution pattern from northwest to southeast. Additionally, Vegetation changes were unstable and concentrated in the western part of the study area (OtogBanner and Otog Front Banner), while the eastern part was stable, in which vegetation improvement took the main position. Moreover, mobile dunes almost disappeared, and semi-fixed dunes decreased and gradually shrank to the west of the sandy area, while fixed dunes soared and were concentrated in the middle of the sandy land. (3) From the soil moisture change, soil moisture at different underground depths showed an overall increasing trend, but the deep soil moisture was higher than the shallow, and spatial distribution varied greatly. (4) From the influencing factors, natural factors significantly influence greening stability, among which precipitation had a particularly profound impact, and interactions with other natural and social factors were higher explanatory. The paper aims to explore whether the ecological environment is developing in a good and orderly direction in the Mu Us Sandy Land, and the potential factors that cause its changes, to provide a theoretical basis for scientific governance in the Mu Us Sandy Land and other arid and semi-arid areas in the future.

To facilitate accurate prediction and empirical research on regional agricultural carbon emissions, this paper uses the LLE-PSO-XGBoost carbon emission model, which combines the Local Linear Embedding (LLE), Particle Swarm Algorithm (PSO) and Extreme Gradient Boosting Algorithm (XGBoost), to forecast regional agricultural carbon emissions in Anhui Province under different scenarios. The results show that the regional agricultural carbon emissions in Anhui Province generally show an upward and then downward trend during 2000-2021, and the regional agricultural carbon emissions in Anhui Province in 2030 are expected to fluctuate between 11,342,100 tones and 14,445,700 tones under five different set scenarios. The projections of regional agricultural carbon emissions can play an important role in supporting the development of local regional agriculture, helping to guide the input and policy guidance of local rural low-carbon agriculture and promoting the development of rural areas towards a resource-saving and environment-friendly society.

Drawing on the framework of Ideological Square Model, this paper carries out a corpus-based analysis of the ways in which group relations and the image of China are re-shaped in the English translation (ET) and the Chinese translation (CT) of Korean news discourse about China with intensifiers as an entry of inquiry. The results show that (1) There is a statistically significant difference regarding faithfulness between the ET and the CT of intensifiers (χ2 = 38.11>3.84, p5.99, P

The construction of an integrated national strategic system and capability is an essential goal of implementing the strategy of military-civilian integration in the contemporary era. And the collaborative innovation of military-civilian S&T is an inevitable choice to achieve this goal. Due to the dynamic, complex, and stochastic characteristics of military-civilian S&T collaborative innovation, the level of S&T innovation is highly volatile. This paper takes the internal and external stochastic disturbance factors of military-civilian S&T collaborative innovation as the perspective, studies the strategy selection problem of military-civilian S&T collaborative innovation under military domination, constructs a differential game model to explore the innovation strategies under the non-cooperative model without military subsidies, the non-cooperative model with military subsidies, and the collaborative model. Finally, we use numerical experiments to verify the validity of the conclusions. The study shows that: (1) Within a reasonable range of values of the benefit distribution coefficient, the system can achieve the Pareto optimum, and the collaborative model is conducive to improving the S&T innovation level and the optimum benefit level of the system. (2) Military subsidies can increase the benefits of the system and the parties involved to achieve Pareto improvement. (3) The level of S&T innovation under the collaborative model has dynamic evolutionary characteristics of maximum expectation and variance. As the intensity of disturbance increases, the stability of the system may be destroyed. Risk-averse civil enterprises prefer the cooperative mode, whereas risk-averse civil enterprises prefer the non-cooperative model.