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This paper presents an innovative methodology to study the application of seasonality (the existence of cyclical patterns) to help predict the level of crime. This methodology combines the simplicity of entropy-based metrics that describe temporal patterns of a phenomenon, on the one hand, and the predictive power of machine learning on the other. First, the classical Colwell’s metrics Predictability and Contingency are used to measure different aspects of seasonality in a geographical unit. Second, if those metrics turn out to be significantly different from zero, supervised machine learning classification algorithms are built, validated and compared, to predict the level of crime based on the time unit. The methodology is applied to a case study in Barcelona (Spain), with month as the unit of time, and municipal district as the geographical unit, the city being divided into 10 of them, from a set of property crime data covering the period 2010-2018. The results show that (a) Colwell’s metrics are significantly different from zero in all municipal districts, (b) the month of the year is a good predictor of the level of crime, and (c) Naive Bayes is the most competitive classifier, among those who have been tested. The districts can be ordered using the Naive Bayes, based on the strength of the month as a predictor for each of them. Surprisingly, this order coincides with that obtained using Contingency. This fact is very revealing, given the apparent disconnection between entropy-based metrics and machine learning classifiers.

Objective To examine the benefits of full-time hospital facility dogs (HFDs) working with qualified nurse handlers for inpatients in a pediatric medical facility. Methods A questionnaire survey on the evaluation of HFD activities was conducted in a hospital that had introduced HFDs for the first time in Japan and has been using them for 9 years. Of the 626 full-time medical staff, 431 responded, of which 270 who observed HFD activities were included in the analysis. The Questionnaire contained 20 questions, and nine questions were selected for presentation in this paper because they focused on the situations in which HFD activities were thought to have a strong impact on inpatients. A comparison of the respondents’ evaluations for each question was made, and differences in the respondents’ attributes (such as profession, length of clinical experience and experience of dog ownership) for those items were examined. Results The impact of HFDs in terminal care was ranked highest among the respondents. Similarly, HFDs increased patient cooperation for clinical procedures. The responses to these two items did not differ statistically depending on the respondents’ attributes. The results imply that patients were more cooperative even for highly invasive examinations and procedures with the support of HFD activities. Conclusions Healthcare providers considered that HFDs were useful, especially for providing support during the terminal phase and for gaining patients’ cooperation for procedures. The fact that the handler was a nurse and the HFD team worked full-time may have enhanced the effectiveness of the program.

Under the background of global urbanization, the continuous expansion and extensive utilization of urban and rural construction land has caused a large amount of arable land to be occupied, which seriously threatens national food security. This paper describes the spatio-temporal patterns of urban and rural construction land expansion and its occupation of arable land by using the urban and rural construction land expansion intensity, the urban and rural construction land expansion intensity difference index, and geo-detector model. It also explores the mechanisms through which the arable land was occupied. Results showed that construction land in both urban and rural areas expanded over the period 2009–2018 despite a large number of rural and urban migrants, and the major contributor to the rapid urbanization in China. This dual expansion could mainly be attributed to the tendency of these migrants to keep or even enlarge their rural construction land, which also resulted in a severer arable land loss than that caused by the expansion of urban construction land. Second, the rate of arable land occupied by urban and rural construction land in Henan province has been gradually slowing down, whereas the expansion of rural construction land is most dependent on arable land occupation. Third, according to the geo-detector model, the relationship between urbanization level and arable land occupied by urban and rural construction was the strongest, followed by the growth rate of fixed asset investment and the proportion of secondary and tertiary industries in GDP.

Background This paper presents findings from a qualitative effectiveness evaluation of an intervention aimed at improving caregiver-young adolescent sexual and reproductive health (SRH) communication including training modules for caregivers on parent-child SRH communication. Methods Data was collected (October 2021-November 2021) using a narrative interviewing technique with thirty caregivers (8 males and 22 females), who received the parent-child communication intervention in Mbarara district, south-western Uganda. We explored caregivers’ experiences with the intervention based on four domains of change: caregiver-young adolescent communication on SRH issues, knowledge and attitudes towards adolescent SRH, parenting skills, and personal life and family. Thematic analysis was used to code and analyse the data, with attention to gender differences. Results Findings highlight positive parenting as a key attribute of SRH communication, along with a transformation of knowledge and attitudes towards the SRH of young adolescents leading to an overall improvement in SRH communication. However, communication is still limited to comfortable topics. Conclusion Our findings indicate improved caregiver–adolescent SRH communication practices following a community intervention. Programming for adolescent health on broader sexuality topics, comfortability and attitude change among caregivers could promote behaviour change on a long term. Future studies may focus on the long term impacts of interventions of this nature and test interventions aimed at addressing comfortability with discussingSRH issues.

The literature on social learning examines how people learn from their neighbors and reach a consensus. The DeGroot social learning model describes the social learning process as one in which people form their opinions by taking a weighted average of their neighbors’ opinions. In the model, the influence structure is represented by a single matrix. In this paper, we empirically identify the role of the hub and authority centralities based on this matrix using data on microfinance adoption in rural Indian villages. Controlling for other well-known centrality measures, authority centrality is positively associated with final adoption rates in the villages, but hub centrality is not. Furthermore, we find that authority centrality is the most informative variable predicting microfinance diffusion success from LASSO regressions.

Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10−3) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping.

We observe an anomaly that SLF quantity expansion is often accompanied by higher interbank market rates. With the Shibor bid panel, this paper empirically shows that SLF easing encourages bank risk-taking activity, and amplifies bank liquidity demand. The induced demand dominates the liquidity supply effect and leads to higher interbank rates. Moreover, the risk-taking behavior of state-owned banks is more sensitive to SLF than that of non-state-owned banks. These features make SLF a better expectation management tool than a price-based or quantity-based tool for interbank market liquidity management.

This paper discusses the merging of two optimization algorithms, atom search optimization and particle swarm optimization, to create a hybrid algorithm called hybrid atom search particle swarm optimization (h-ASPSO). Atom search optimization is an algorithm inspired by the movement of atoms in nature, which employs interaction forces and neighbor interaction to guide each atom in the population. On the other hand, particle swarm optimization is a swarm intelligence algorithm that uses a population of particles to search for the optimal solution through a social learning process. The proposed algorithm aims to reach exploration-exploitation balance to improve search efficiency. The efficacy of h-ASPSO has been demonstrated in improving the time-domain performance of two high-order real-world engineering problems: the design of a proportional-integral-derivative controller for an automatic voltage regulator and a doubly fed induction generator-based wind turbine systems. The results show that h-ASPSO outperformed the original atom search optimization in terms of convergence speed and quality of solution and can provide more promising results for different high-order engineering systems without significantly increasing the computational cost. The promise of the proposed method is further demonstrated using other available competitive methods that are utilized for the automatic voltage regulator and a doubly fed induction generator-based wind turbine systems.

The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches.

Most existing secure biometric authentication schemes are server-centric, and users must fully trust the server to store, process, and manage their biometric data. As a result, users’ biometric data could be leaked by outside attackers or the service provider itself. This paper first constructs the EDZKP protocol based on the inner product, which proves whether the secret value is the Euclidean distance of the secret vectors. Then, combined with the Cuproof protocol, we propose a novel user-centric biometric authentication scheme called BAZKP. In this scheme, all the biometric data remain encrypted during authentication phase, so the server will never see them directly. Meanwhile, the server can determine whether the Euclidean distance of two secret vectors is within a pre-defined threshold by calculation. Security analysis shows BAZKP satisfies completeness, soundness, and zero-knowledge. Based on BAZKP, we propose a privacy-preserving biometric authentication system, and its evaluation demonstrates that it provides reliable and secure authentication.

Although both the analysis of regional culture and urbanisation are long-standing preoccupations in geography, few studies have considered the relationship between the two, the former traditionally being a topic in cultural geography, while the latter is usually interpreted and analysed as a process in economic geography. Taking evidence from the 1851–1911 censuses of England and Wales, this article analyses individual migration paths to identify stable regions of human interaction by applying a sophisticated community-detection algorithm. By accurately mapping the regions within which the majority of migration occurred between 1851 and 1911 and arguing that the stability of these geographies is evidence of more than just mutable communities but rather of persistent regional cultures, this article responds to previous studies that have sought to identify the cultural provinces of England and Wales. Indeed, by demonstrating that the regions bear a striking resemblance to those that have long been hypothesised as being distinct cultural provinces of England and Wales, this article empirically corroborates their existence. In order to further demonstrate that the regions constitute cultural provinces, this paper incorporates these boundaries into a spatial interaction model (SIM). The results of the SIM not only shows that the boundaries between the regions limited the number of migrants that crossed them–over and above that explained by control variables–and therefore represented the boundaries of cultural provinces, demarcating discrete regions of human interaction–but that such boundaries disproportionately restricted rural-urban migrants, thereby slowing the pace at which England and Wales urbanised. This paper therefore demonstrates that urbanisation should not only be interpreted as only an economic phenomenon, but a cultural one also, and that if urbanisation is to be fully understood, individuals’ attachment to place as a component of their identity, ought to be formally incorporated into models of migration.

Background There is a global shortage of midwives, whose services are essential to meet the healthcare needs of pregnant women and newborns. Evidence suggests that if enough midwives, trained and regulated to global standards, were deployed worldwide, maternal, and perinatal mortality would decline significantly. Health workforce planning estimates the number of midwives needed to achieve population coverage of midwifery interventions. However, to provide a valid measure of midwifery care coverage, an indicator must consider not only the raw number of midwives, but also their scope and competency. The tasks midwives are authorized to deliver and their competency to perform essential skills and behaviors provide crucial information for understanding the availability of safe, high-quality midwifery services. Without reliable estimates for an adequate midwifery workforce, progress toward ending preventable maternal and perinatal mortality will continue to be uneven. The International Labor Organization (ILO) and the International Confederation of Midwives (ICM) suggest standards for midwifery scope of practice and competencies. This paper compares national midwifery regulations, scope, and competencies in three countries to the ILO and ICM standards to validate measures of midwife density. We also assess midwives’ self-reported skills/behaviors from the ICM competencies and their acquisition. Methods and findings We compared midwives’ scope of practice in Argentina, Ghana, and India to the ILO Tasks and ICM Essential Competencies for Midwifery Practice. We compared midwives self-reported skills/behaviors with the ICM Competencies. Univariate and bivariate analysis was conducted to describe the association between midwives’ skills and selected characteristics. National scopes of practice matched two ILO tasks in Argentina, four in India, and all in Ghana. National standards partially reflected ICM skills in Categories 2, 3, and 4 (pre-pregnancy and antenatal care; care during labor and birth; and ongoing care of women and newborns, respectively) in Argentina (range 11% to 67%), mostly in India (range 74% to 100%) and completely in Ghana (100% match). 1,266 midwives surveyed reported considerable variation in competency for skills and behaviors across ICM Category 2, 3, and 4. Most midwives reported matching skills and behaviors around labor and childbirth (Category 2). Higher proportions of midwives reported gaining basic skills through in-service training and on-job-experience than in pre-service training. Conclusion Estimating the density of midwives needed for an adequate midwifery workforce capable of providing effective population coverage is predicated on a valid numerator. A reliable and valid count of midwives to meet population needs assumes that each midwife counted has the authority to exercise the same behaviors and reflects the ability to perform them with comparable competency. Our results demonstrate variation in midwifery scopes of practice and self-reported competencies in comparison to global standards that pose a threat to the reliability and validity of the numerator in measures of midwife density, and suggest the potential for expanded authorization and improved education and training to meet global reference standards for midwifery practice has not been fully realized. Although the universally recognized standard, this study demonstrates that the complex, composite descriptions of skills and behaviors in the ICM competencies make them difficult to use as benchmark measures with any precision, as they are not defined or structured to serve as valid measures for assessing workforce competency. A simplified, content-validated measurement system is needed to facilitate evaluation of the competency of the midwifery workforce.

This paper uses an infrared high-speed motion capture system based on deep learning to analyze difficult movements, which helps aerobics athletes master difficult movements more accurately. Firstly, changes in joint angle, speed of movement, and ground pressure are used to analyze the impact and role of motion fluency and completion based on a biomechanical perspective. Moreover, based on the existing infrared high-speed motion capture systems, the Restricted Boltzmann Machine (RBM) model is introduced to construct an unsupervised similarity framework model. Next, the motion data is reorganized based on three-dimensional information to adapt to the model’s input. Then, the framework performs similar frame matching to obtain a set of candidate frames that can be used as motion graph nodes. After the infrared high-speed motion capture system and inertial sensors are simultaneously applied to subjects, the multi-correlation coefficients (CMC) values of the hip, knee, and ankle angles are 0.94 ± 0.06, 0.98 ± 0.01, and 0.87 ± 0.09, respectively. The two systems show a high degree of correlation in the measurement results, and the knee joint is the most significant correlation. Finally, a motion graph is constructed to control its trajectory and adjust its motion pattern. The infrared high-speed motion capture system optimized for deep learning can extract features from human bone data and capture motion more accurately, helping trainers to fully understand difficult movements.

Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities.

Background Traumatic brain injuries (TBI) are a significant cause of mortality and morbidity for children globally. Adherence to evidence-based treatment guidelines have been shown to improve TBI outcomes. To inform the creation of a pediatric TBI management guideline for a low and middle income country context, we assessed the quality of available clinical practice guidelines (CPGs) for the acute management pediatric TBI. Methods Articles were identified and retrieved from MEDLINE, EMBASE, Cochrane Library, LILACS, Africa-Wide Information and Global Index Medicus. These articles were screened by four reviewers independently. Based on the eligibility criteria, with the exception of literature reviews, opinion papers and editor’s letters, articles published from 1995 to November 11, 2016 which covered clinical recommendations, clinical practice or treatment guidelines for the acute management of pediatric TBI (within 24 hours) were included for review. A reference and citation analysis was performed. Seven independent reviewers from low, middle and high income clinical settings with knowledge of pediatric TBI management appraised the guidelines using the AGREE II instrument. Scores for the CPGs were aggregated by domain and overall assessment was determined. Results We screened 2372 articles of which 17 were retained for data extraction and guideline appraisal. Except for one CPG from a middle income country, the majority (16/17) of the guidelines were developed in high income countries. Seven guidelines were developed specifically for the pediatric population, while the remaining CPGs addressed the acute management of TBI in both adult and pediatric populations. The New Zealand Guideline Group (NZGG, 2006) received the highest overall assessment score of 46/49 (93.88%) followed by the Scandinavian Neurotrauma Committee (SNC, 2016) with a score of 45/49 (91.84%) followed by the Scottish Intercollegiate Guideline Network (SIGN, 2009) and Brain Trauma Foundation (BTF 2012) both with scores of 44/49 (89.80%). CPGs from Cincinnati Children’s Hospital (CCH 2006) and Sao Paulo Medical School Hospital/Brazilian Society of Neurosurgery (USP/BSN, 2001) received the lowest score of 27/49 (55.10%) subsequently followed by the Appropriateness Criteria (ACR, 2015) with 29/49 (59.18%). The domains for scope and purpose and clarity of presentation received the highest scores across the CPGs, while applicability and editorial independence domains had the lowest scores with a wider variability in score range for rigor of development and stakeholder involvement. Conclusions To our knowledge, this is the first systematic review and guideline appraisal for pediatric CPGs concerning the acute management of TBI. Targeted guideline creation specific to the pediatric population has the potential to improve the quality of acute TBI CPGs. Furthermore, it is crucial to address the applicability of a guideline to translate the CPG from a published manuscript into clinically relevant local practice tools and for resource limited practice settings.

Presently, the Strait of Gibraltar is undergoing an unprecedented invasion of the alien alga Rugulopteryx okamurae of North Pacific origin. According to the scarce literature, the algae first settled in the south shore, probably following commercial exchanges with French ports where it was accidentally introduced together with Japanese oysters imported for mariculture. There is no certainty, however, that the algae first colonized the south shore of the Strait and, from there, spread to the north. It could well have been the opposite. Whatever the case, it spread all over the Strait and surrounding areas with amazing rapidity. Human-mediated vectors (algae attached to ship hulls or fishing nets, for example) can be behind the spread from the shore initially settled to the algae-free shore on the opposite side. But it could also have happened by means of hydrodynamic processes without direct human intervention. This possibility is assessed in this paper by revisiting historical current meter profiles collected in the Strait of Gibraltar searching for secondary cross-strait flows. All the stations present an intermediate layer of northward cross-strait velocity near the interface of the mean baroclinic exchange along with a surface layer above of southward velocity, whose lower part also overlaps the interface zone. The first one would back the south-to-north transport of algal fragments, the second one, the north-to south. In both cases, algae must reach the depth of the interface. The vertical velocity field in the area, which far exceeds the small sedimentation velocity of the algae, allows their vertical displacements throughout the water column. Its endurance to survive under the weak or no light conditions that will prevail during the cross-strait transport and its capability of reactivating the metabolism after this unfavorable period, offers chances for colonizing the opposite shore. Therefore, the propagation of the algae by hydrodynamic processes, without human intervention, cannot be ruled out.

Background In 2020, Portugal had high levels of unmet health care needs. Primary Care was reported as the main source of unmet needs. Objectives To describe face-to-face and remote access to GPs in Portugal during the COVID-19 pandemic. To discover patient experiences and attitudes to access to care. To identify determinants of access to care. Methods A survey of a random sample of 4,286 adults registered in a group of Family Practices was conducted in 2021. Paper questionnaires were sent by post to patients who had no e-mail address registered with the practice. Patients with an e-mail address were sent a link to an online questionnaire. Outcomes were reported waiting times for face-to-face and remote contacts with GPs, dichotomized to ascertain compliance with standards. Associations between participant characteristics and outcome variables were tested using logistic regression. Results Waiting times for face-to-face consultations with GPs during the pandemic often exceeded the maximum waiting times (MWT) set by the National Health Service. Remote contacts were mostly conducted within acceptable standards. Waiting times for speaking with the GP over the phone were rated as ‘poor’ by 40% and 27% reported requests for these calls as unmet. The odds of getting care over MWT increased for participants who reported poorer digital skills. Participants were less likely to get non-urgent consultations over MWT if they found it easy to use the online patient portal to book appointments (odds ratio 0.24; 99% confidence intervals 0.09–0.61), request prescriptions (0.18; 0.04–0.74) or insert personal data (0.18; 0.04–0.95). Conclusion Patient reported access to GPs during the pandemic was uneven in Portugal. Obtaining non-urgent consultations and remote contacts over MWT affected mainly those patients with poor digital skills. Telephone access to GPs received the worse ratings. Access through traditional pathways must remain available, to prevent the widening of inequities.

Nowadays, Wireless Sensor Networks (WSNs) are widely used for collecting, communicating, and sharing information in various applications. Due to its limited resources in terms of computation, power, battery lifetime, and memory storage for sensor nodes, it is difficult to add confidentiality and integrity security features. It is worth noting that blockchain (BC) technology is one of the most promising technologies, because it provides security, avoids centralization, and a trusted third party. However, to apply BCs in WSNs is not an easy task because BC is typically resource-hungry for energy, computation, and memory. In this paper, the additional complication of adding BC in WSNs is compensated by an energy minimization strategy, which basically depends on minimizing the processing load of generating the blockchain hash value, and encrypting and compressing the data that travel from the cluster-heads to the base station to reduce the overall traffic, leading to reduced energy per node. A specific (dedicated) circuit is designed to implement the compression technique, generate the blockchain hash values and data encryption. The compression algorithm is based on chaotic theory. A comparison of the power consumed by a WSN using a blockchain implementation with and without the dedicated circuit, illustrates that the hardware design contributes considerably to reduce the consumption of power. When simulating both approaches, the energy consumed when replacing functions by hardware decreases up to 63%.

With the success of pre-trained language models, the performance of story ending generation has been dramatically improved while remaining challenging due to the lack of commonsense reasoning ability. Most previous works mainly focus on using commonsense knowledge to enhance the implicit correlations between words but ignore the hidden causality of sentences or events. In this paper, we propose Causal commonsense Enhanced joint model for story ending Generation (CEG), which incorporates causal commonsense events knowledge to generate a reasonable story ending. Specifically, we first develop a commonsense events inference model trained on GLUCOSE, which converts static knowledge into a dynamic generation model to discover unseen knowledge. It uses prompts to produce various commonsense events behind the stories as pseudo-labels of the dataset. Then, we propose a joint model for the causal events inference task and the story ending generation task to inject inference knowledge into the generation, which consists of a shared encoder, an inference decoder, and a generation decoder. In the causal events inference task, we use the shared encoder and the inference decoder to reason the causal events behind each sentence of the story context to help the model better understand the story and provide long-distance dependencies for the story ending generation. In story ending generation, we combine the hidden states of the causal events with the story context to generate the story ending by the shared encoder and the generation decoder. We jointly train the model on two tasks so that the generation decoder produces the story endings that better match the clues. Experimental results on the ROCStories dataset show that our model outperforms the previous works, demonstrating the effectiveness of the joint model and the generated causal events.

Introduction Poor psychosocial support and lack of respectful care for women during childbirth are commonplace in health facilities in low- and middle-income countries. While WHO recommends providing supportive care to pregnant women, there is a scarcity of material for building the capacity of maternity staff to provide systematic and inclusive psychosocial support to women in the intrapartum phase, and prevent work stress and burnout in maternity teams. To address this need we adapted WHO’s mhGAP for maternity staff to provide psychosocial support in labour room settings in Pakistan. Mental Health Gap Action Programme (mhGAP) is an evidence-based guidance which provides psychosocial support in resource-limited health care settings. This paper aims to describe the adaptation of mhGAP to develop psychosocial support capacity building materials for maternity staff to provide support to maternity patients, and also to staff, in the labour room context. Methods Adaptation was conducted within the Human-Centered-Design framework in three phases: inspiration, ideation, and implementation feasibility. In inspiration, a review of national-level maternity service-delivery documents and in-depth interviews of maternity staff were conducted. Ideation involved a multidisciplinary team to develop capacity-building materials by adapting mhGAP. This phase was iterative and included cycles of pretesting, deliberations, and revision of materials. In implementation feasibility, materials were tested via the training of 98 maternity staff and exploring system feasibility via post-training visits to health facilities. Results Inspiration phase identified gaps in policy directives and implementation and formative study identified limited understanding and skills of staff to assess patients’ psychosocial needs and provide appropriate support. Also, it became evident that staff themselves needed psychosocial support. In ideation, team developed capacity-building materials comprising two modules: one dedicated to conceptual understanding, the other to implementing psychosocial support in collaboration with maternity staff. In implementation feasibility, staff found the materials relevant and feasible for the labour room setting. Finally, users and experts endorsed usefulness of the materials. Conclusion Our work in developing psychosocial-support training materials for maternity staff extends the utility of mhGAP to maternity care settings. These materials can be used for capacity-building of maternity staff and their effectiveness can be assessed in diverse maternity care settings.

Sustainable development is the theme of world economic development in the 21st century. As a key part of sustainable development, sustainable land use (SLU) encompasses economic development and environmentally friendly and social progress. In recent decades, China has formulated many environmental regulatory policies to achieve sustainable development and "carbon peaking and carbon neutrality (double-carbon)" goals, among which the carbon emission trading scheme (CETS) is the most representative and provides valuable research. In this paper, we aimed to reflect the spatio-temporal evolution of SLU in China under the influence of environmental regulatory policies through an indicator measurement strategy based on the DID estimation method. The study conclusions are as follows: (1) The CETS can effectively improve SLU from the perspectives of economic development and environmentally friendly progress, and the impact has primarily been in the pilot areas. And, its effectiveness is closely linked to local locational factors. (2) With respect to the dimension of economic development, the CETS has not changed the provincial distribution patterns of SLU; rather, it continues to remain "high to low, east to west". However, regarding the environmentally friendly progress dimension, the CETS has significantly changed the provincial distribution patterns of SLU, which are characterized by spatial agglomeration with urban agglomerations such as the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) as the core. (3) The screening results of the SLU indicators based on economic development showed that the CETS primarily improved the innovation capacities of pilot regions, and the impacts on economic levels were relatively small. Similarly, the screening results of the SLU indicators based on environmentally friendly progress showed that the CETS had primarily acted on reducing pollution emission intensity and strengthening greening construction, revealing only short-term effects on improving energy use efficiency. Based on the above, this paper explored the meaning and role of the CETS in more detail, with a view to providing insight into the implementation and formulation of environmental regulation policies.

Based on the panel data of 30 provinces in China from 2011 to 2019, this paper uses a two-stage DEA model to measure regional innovation efficiency, then non-parametric test is used to examine the impact of innovation network structure and government R&D investment on regional innovation efficiency. The results show that, at the provincial level, innovation efficiency of regional R&D is not necessarily in direct proportion to the innovation efficiency in the commercialization stage. Commercialization efficiency is not necessarily high in provinces with high technical R&D efficiency. At the national level, the innovation efficiency gap between our country’s R&D and commercialization stage is small, indicating that the development of the national innovation efficiency is more and more balanced. Innovation network structure can promote the R&D efficiency, but has no significant effect on the commercialization efficiency. Government R&D investment helps to improve the R&D efficiency, but it is not conducive to the improvement of commercialization efficiency. The interaction between innovation network structure and government R&D investment will have compound effects on regional innovation efficiency; the region with underdeveloped innovation network structure can increase the government R&D investment to make it have a higher level of R&D. This paper provides insights into how to improve innovation efficiency in different social networks and policy environments.

The negative impact of the financialization of non-financial firms cannot be ignored in China. However, existing studies neglect that the government environmental governance is an important influential factor in corporate investment decisions. Using a sample of China’s non-financial listed firms from 2007 to 2020, we examine the impact of local governments’ energy-saving target constraints on the financialization of local firms in terms of whether local governments set numerically specific energy-saving targets in the Government Work Reports. The main findings of this paper are as follows. First, local governments setting clear energy-saving targets inhibit local firms’ financialization and the result holds even after a series of robustness tests. Second, the negative association between local governments’ energy-saving target constraints and firm financialization is more pronounced among firms in eastern regions and green provinces. Third, the quality of firm information disclosure and local environmental public supervision enhance the inhibiting effect of local governments’ energy-saving target constraints on firm financialization. Fourth, local governments’ energy-saving target constraints restrain firm financialization by attracting more external analyst coverage and encouraging internal technological innovation. Moreover, this inhibiting effect can help reduce overinvestment and improve the total factor productivity of firms. Our study provides evidence supporting firm financialization studies from the novel perspective of government environmental governance.

Mortise and tenon joints are widely used in the building and furniture industries because of their excellent mechanical and eco-friendly properties. In real-life cases, there are usually many available alternative structures for a joint area, it is a challenge to select a proper structure from massively available alternatives. This paper aims to select a proper multiple attribute decision-making method based on massive alternatives and unreliable, uncertain and subjective information. Pugh’s controlled convergence, rough number, Z-number, consistency theory and Shannon entropy are integrated and proposed an improved rough Z-number Multi-Attributive Border Approximation Area Comparison (MABAC) method. Firstly, Pugh’s controlled convergence is a selection method, simple and rapid, presented in the first phase to eliminate most of the alternatives. In the second phase, an integrated method is proposed. The consistency theory, distance measurement and the Z-number are initially aggregated to calculate the expert weight. The entropy method is then presented to determine the criteria weight. The alternatives are then ranked and the optimal mortise and tenon joint is selected based on the rough Z-number MABAC method. A real-life case is presented, and the proposed method is implemented in the joint of a bucket cabinet. Finally, the efficiency and effectiveness of the proposed method are proved by the case, sensitivity analysis and related comparisons.

Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life, reading from a screen has become widely used. Several studies point to challenges in processing written materials from the screen due to changes in attention allocation when reading from a screen compared to reading from a printed paper. The current study examined the differences in brain activation when reading from a screen compared to reading from a printed paper focusing on spectral power related to attention in fifteen 6-8-year-old children. Using an electroencephalogram, children read two different age-appropriate texts, without illustrations, presented randomly on the screen and on a printed paper. Data were analyzed using spectral analyses in brain regions related to language, visual processing, and cognitive control, focusing on theta vs. beta waveforms. Results indicated that while reading from a printed paper was accompanied by higher energy in high-frequency bands (beta, gamma), reading from the screen was manifested by a higher power in the lower frequency bands (alpha, theta). Higher theta compared to the beta ratio, representing challenges in allocating attention to a given task, was found for the screen reading compared to the printed paper reading condition. Also, a significant negative correlation was found between differences in theta/beta ratio for screen vs paper reading and accuracy level in the age-normalized Sky-Search task measuring attention and a positive correlation with performance time. These results provide neurobiological support for the greater cognitive load and reduced focused attention during screen-based compared to print-based reading and suggest a different reliance on attention resources for the two conditions in children.

The grey prediction is a common method in the prediction. Studies show that general grey models have high modeling precision when the time sequence varies slowly, but some grey models show low modeling precision for the high-growth sequence. The paper researches the grey modeling for the high-growth sequence using the extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α). To improve the nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α)’s prediction precision and make data have better adaptability to the model, the paper makes improvements in the following three aspects: (1) the paper improves the accumulated generating sequence of original time sequence, i.e. making a new transformation of traditional accumulated generating sequence; (2) the paper improves the model’s structure, extends the grey action and builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α); (3) the paper improves the model’s background value and uses the value of cubic spline function to approximate the background value. Because the parameters of the new accumulated generating sequence transformed, the nonlinear grey Bernoulli model’s time response equation and the background value are optimized simultaneously, the prediction precision increases greatly. The paper builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃2,α) using the method proposed and seven comparison models for China’s express delivery volume per capita. Comparison results show that the extended nonlinear grey Bernoulli model built with the method proposed has high simulation and prediction precision and shows the precision superior to that of seven comparison models.

Objectives Approximately 10% of people with intellectual disability display aggressive challenging behaviour, usually due to unmet needs. There are a variety of interventions available, yet a scarcity of understanding about what mechanisms contribute to successful interventions. We explored how complex interventions for aggressive challenging behaviour work in practice and what works for whom by developing programme theories through contexts-mechanism-outcome configurations. Methods This review followed modified rapid realist review methodology and RAMESES-II standards. Eligible papers reported on a range of population groups (intellectual disability, mental health, dementia, young people and adults) and settings (community and inpatient) to broaden the scope and available data for review. Results Five databases and grey literature were searched and a total of 59 studies were included. We developed three overarching domains comprising of 11 contexts-mechanism-outcome configurations; 1. Working with the person displaying aggressive challenging behaviour, 2. Relationships and team focused approaches and 3. Sustaining and embedding facilitating factors at team and systems levels. Mechanisms underlying the successful application of interventions included improving understanding, addressing unmet need, developing positive skills, enhancing carer compassion, and boosting staff self-efficacy and motivation. Conclusion The review emphasises how interventions for aggressive challenging behaviour should be personalised and tailored to suit individual needs. Effective communication and trusting relationships between service users, carers, professionals, and within staff teams is essential to facilitate effective intervention delivery. Carer inclusion and service level buy-in supports the attainment of desired outcomes. Implications for policy, clinical practice and future directions are discussed. Prospero registration number CRD42020203055.

Introduction Military healthcare studies have reported a wide range of mental health issues amongst military personnel. Globally, mental health issues are one of the main causes of ill health. Military personnel have a greater prevalence of mental health issues than that of the general population. The impact of mental health issues can be wide and far reaching for family and carers. This systematic narrative review explores the military spouse experience of living alongside their serving or veteran partner with a mental health issue. Methods The systematic review performed was based on the PRISMA guide for searching, screening, selecting papers for data extraction and evaluation. Studies were identified from CINHAL, ASSIA, Proquest Psychology, Proquest Nursing & Allied Health source, Proquest Dissertations & Theses, ETHOS, PsychArticles, Hospital collection, Medline, Science Direct Freedom Collection and hand searching of citations and reference lists. Results Twenty-seven studies were included in the narrative synthesis. Five overarching themes from the experiences of military spouses’ living alongside their serving/veteran partners mental health issue were identified: caregiver burden, intimate relationships, psychological/psychosocial effects on the spouse, mental health service provision and spouse’s knowledge and management of symptoms. Conclusions The systematic review and narrative synthesis identified that the majority of studies focused on spouses of veterans, very few were specific to serving military personnel, but similarities were noted. Findings suggest that care burden and a negative impact on the intimate relationship is evident, therefore highlight a need to support and protect the military spouse and their serving partner. Likewise, there is a need for greater knowledge, access and inclusion of the military spouse, in the care and treatment provision of their serving partner’s mental health issue.

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future.

Introduction The vertical dimensional arrangement of space is primarily influenced by the reachable range of human fingertips. Currently, this dimensional layout analysis concentrates solely on static body dimensions based on Farley’s principle. However, the joint mobility of the elderly population has diminished, necessitating the identification of the factors influencing the age-friendly spatial vertical dimension layout through experimentation. Methods A random sample of 62 adults and 62 elderly individuals were selected to measure the joint mobility of 8 groups with varying comfort levels. The measurement results were analyzed using an independent sample t-test with SPSS software. Results The joint mobility of the elderly group exhibited a notable difference from that of the adult group across distinct comfort gradients. A significant reduction in the range of motion of all joints in the elderly was observed. The findings suggest that the reachable range of the upper limbs of the elderly should be considered in conjunction with joint mobility. We propose a vertical dimensional layout method for residential spaces that accommodates the joint mobility of elderly users. Conclusion The joint mobility of the elderly is significantly deteriorating, and the traditional vertical spatial layout method cannot adequately support the daily life of the elderly. It is essential to integrate the joint mobility factor into the vertical dimensional layout design process. In this paper, we propose an elderly-friendly vertical spatial dimension layout method. It offers a reference for subsequent planning of elderly-friendly vertical dimension layout.

Currently, platforms (such as Amazon.com and JD.com) are gradually transitioning from pure resellers to platforms providing hybrid channel structures. In a hybrid channel, the reselling channel and the agency channel on the platform are simultaneously used. Therefore, according to the agent who sells through the agency channel (manufacturer or third-party retailer), two kinds of hybrid channel structurers can be selected by the platform. At the same time, due to the intense competition caused by the hybrid channel structure, platforms take the initiative to implement the product quality distribution strategy in which different quality products are sold through various retailing channels. Thus, from the perspective of platforms, how to coordinate the selection of hybrid channel structures and the implementation of the product quality distribution strategy is a significant issue that has been overlooked in existing literature. This paper proposes game-theoretic models to investigate whether a platform should choose “which” hybrid channel structure to use and decide “whether” to adopt the product quality distribution strategy. Our analysis shows that the game equilibrium is affected by the commission rate, the product differentiation level, and the production cost. More specifically, first, it is interestingly found that if the product differentiation level exceeds a particular threshold, the product quality distribution strategy can negatively influence the retailer to abandon the hybrid retailing mode. In contrast, the manufacturer continues to sell through the agency channel as a part of the product distribution plan. Second, regardless of the channel configuration, we find that the platform chooses to increase the order quantity with the help of the product distribution plan. Third, contrary to popular belief, the product quality distribution strategy can only benefit the platform when the third-party retailer participates in hybrid retailing with a suitable commission rate and product differentiation level. Fourth, the platform should make decisions on the above two strategies concurrently; otherwise, agency sellers (manufacturer or third-party retailer) will oppose the product quality distribution strategy. Our key findings can assist stakeholders in making strategic decisions about hybrid retailing modes and product distribution.

Non-local dependency in Mandarin wh-questions has been extensively researched in theoretical linguistics, but it remains an under-studied topic in the field of language processing. Unlike languages that require wh-movement to form wh-questions, Mandarin is a wh-in-situ language, and hence is generally assumed to require a covert dependency between a wh-phrase and its scope-bearing position. Mandarin therefore provides an ideal linguistic environment in which to study not only cognitive-processing mechanisms, but also how different types of non-local dependency, especially covert dependency, can be handled by readers. This paper investigates the processing of such covert non-local dependency in multiple embedded clauses, that is, multiple complementizer phrases (CPs). In wh-in-situ sentences with multiple CPs, the wh-phrases’ scope varies according to the types of verbs and their embedded clauses. Based on the subcategorization of clausal verbs, we designed four experimental conditions: double-embedded low scope, double-embedded high scope, double-embedded ambiguous scope, and long distance in pivotal construction. According to memory-based and distance-based language processing theories, the low-scope condition should be easier to process than the high-scope one, because the former has a shorter linear distance than the latter when forming dependencies; and pivotal construction should be easier to process than high-scope embedded clauses, because the former has a shorter structural distance. In cases where both low- and high-scope interpretations are possible, we aim to determine whether readers exhaust every potential interpretation during comprehension, or adopt a ‘good-enough’ approach to obtaining an interpretation via an easier and less costly process. To this end, we will adopt the eye-tracking technique that allows us to obtain fine-grained reading-time data, which can be used to compare processing across conditions. The results will contribute to understanding human readers’ mechanisms for processing covert dependency and resolving scope ambiguity in wh-in-situ languages.

This paper studies the environmentally sustainable investment of an agricultural supply chain composed of a farmer and a company, under three subsidy policies which are the non-subsidy policy, the fixed subsidy policy, and the Agriculture Risk Coverage (ARC) subsidy policy. Then, we analyse the impact of different subsidy policy and adverse weather on the costs of the government and profits of the farmer and the company. By comparing with the non-subsidy policy, we find that both the fixed subsidy policy and the ARC policy encourage the farmer to improve the environmentally sustainable investment level and increase the profit of the farmer and the company. We also find that both the fixed subsidy policy and the ARC subsidy policy lead to an increase in government spending. Our results show that the ARC subsidy policy has a significate advantage in encouraging the farmer’s environmentally sustainable investment if the adverse weather is relatively serious, comparing with the fixed subsidy policy. In turn, our results also show that the ARC subsidy policy is more beneficial for both the farmer and the company than the fixed subsidy policy if the adverse weather is relatively serious, which then leads to a higher expenditure of the government. Therefore, our conclusion serves as a theoretical basis for governments to formulate agricultural subsidy policies and promote sustainable development of the agricultural environment.

Background Early intervention alcohol and drug (AOD) programs for disadvantaged young people have the potential to substantially decrease the need for future intervention, however there is little research about how young people use these programs or the substance use and other outcomes of such programs. This paper uses data from an Australian AOD early intervention program, The Street Universities, to: describe young people’s participation; examine changes in substance use and wellbeing over 90 days; and assess which young people are most positively impacted Methods Data come from a prospective study of new attendees, measuring retention in and attendance patterns in an ‘engagement’ program focussed on arts and lifestyle activities (n = 95), and a routine service dataset collected from seven years of therapeutic intervention (n = 3,893), measuring substance dependence (SDS), psychological distress (K10) and quality of life (EQoL) Results Analysis reveals that young people were retained in the program at high proportion (63% at six months) and more than half of these returned at a frequency of weekly or more often. Young people participating in the therapeutic component of the program reported significant improvements in all key wellbeing indicators with SDS, K10 and EQoL scores significantly improving (at p < .001). These improvements occurred rapidly, within the first 30 days, and were maintained over the 90 day study period. Moreover, young people with the highest SDS and K10 scores and lowest quality of life at baseline experienced the most positive changes. Conclusion Aligning engagement program with therapeutic intervention can provide comprehensive support to disadvantaged young people, producing substantial improvements in AOD use, distress and wellbeing.

Introduction The number of children with mental health problems has more than doubled since the COVID-19 pandemic. However, the effect of long Covid on children’s mental health is still debatable. Recognising long Covid as a risk factor for mental health problems in children will increase awareness and screening for mental health problems following COVID-19 infection, resulting in earlier intervention and lower morbidity. Therefore, this study aimed to determine the proportion of mental health problems post-COVID-19 infection in children and adolescents, and to compare them with the population with no previous COVID-19 infection. Methodology A systematic search was done in seven databases using pre-defined search terms. Cross-sectional, cohort and interventional studies reporting the proportion of mental health problems among children with long COVID in the English language from 2019 to May 2022 were included. Selection of papers, extraction of data and quality assessment were done independently by two reviewers. Studies with satisfactory quality were included in meta-analysis using R and Revman software programmes. Results The initial search retrieved 1848 studies. After screening, 13 studies were included in the quality assessments. Meta-analysis showed children who had previous COVID-19 infection had more than two times higher odds of having anxiety or depression, and 14% higher odds of having appetite problems, compared to children with no previous infection. The pooled prevalence of mental health problems among the population were as follows; anxiety: 9%(95% CI:1, 23), depression: 15%(95% CI:0.4, 47), concentration problems: 6%(95% CI: 3, 11), sleep problems: 9%(95% CI:5, 13), mood swings: 13% (95%CI:5, 23) and appetite loss: 5%(95% CI:1, 13). However, studies were heterogenous and lack data from low- and middle-income countries. Conclusion Anxiety, depression and appetite problems were significantly increased among post-COVID-19 infected children, compared to those without a previous infection, which may be attributed to long COVID. The findings underscore the importance of screening and early intervention of children post-COVID-19 infection at one month and between three to four months.

Several optimization problems can be abstracted into many-objective optimization problems (MaOPs). The key to solving MaOPs is designing an effective algorithm to balance the exploration and exploitation issues. This paper proposes a novel many-objective African vulture optimization algorithm (MaAVOA) that simulating the African vultures’ foraging and navigation behaviours to solve the MaOPs. MaAVOA is an updated version of the African Vulture Optimization Algorithm (AVOA), which was recently proposed to solve the MaOPs. A new social leader vulture for the selection process is introduced and integrated into the proposed model. In addition, an environmental selection mechanism based on the alternative pool is adapted to improve the selection process to maintain diversity for approximating different parts of the whole Pareto Front (PF). The best-nondominated solutions are saved in an external Archive based on the Fitness Assignment Method (FAM) during the population evolution. FAM is based on a convergence measure that promotes convergence and a density measure that promotes variety. Also, a Reproduction of Archive Solutions (RAS) procedure is developed to improve the quality of archiving solutions. RAS has been designed to help reach out to the missing areas of the PF that the vultures easily miss. Two experiments are conducted to verify and validate the suggested MaAVOA’s performance efficacy. First, MaAVOA was applied to the DTLZ functions, and its performance was compared to that of several popular many-objective algorithms and according to the results, MaAVOA outperforms the competitor algorithms in terms of inverted generational distance and hypervolume performance measures and has a beneficial adaptation ability in terms of both convergence and diversity performance measures. Also, statistical tests are implemented to demonstrate the suggested algorithm’s statistical relevance. Second, MaAVOA has been applied to solve two real-life constrained engineering MaOPs applications, namely, the series-parallel system and overspeed protection for gas turbine problems. The experiments show that the suggested algorithm can tackle many-objective real-world applications and provide promising choices for decision-makers.

Background Over a third of pregnant women (around 250,000) each year in the United Kingdom have experienced trauma such as domestic abuse, childhood trauma or sexual assault. These experiences can have a long-term impact on women’s mental and physical health. This global qualitative evidence synthesis explores the views of women and maternity care professionals on routine discussion of previous trauma in the perinatal period. Methods Systematic database searches (MEDLINE, EMBASE, CINAHL Plus, APA PsycINFO and Global Index Medicus) were conducted in July 2021 and updated in April 2022. The quality of each study was assessed using the Critical Appraisal Skills Programme. We thematically synthesised the data and assessed confidence in findings using GRADE-CERQual. Results We included 25 papers, from five countries, published between 2001 and 2022. All the studies were conducted in high-income countries; therefore findings cannot be applied to low- or middle-income countries. Confidence in most of the review findings was moderate or high. The findings are presented in six themes. These themes described how women and clinicians felt trauma discussions were valuable and worthwhile, provided there was adequate time and appropriate referral pathways. However, women often found being asked about previous trauma to be unexpected and intrusive, and women with limited English faced additional challenges. Many pregnant women were unaware of the extent of the trauma they have suffered, or its impact on their lives. Before disclosing trauma, women needed to have a trusting relationship with a clinician; even so, some women chose not to share their histories. Hearing trauma disclosures could be distressing for clinicians. Conclusion Discussions of previous trauma should be undertaken when women want to have the discussion, when there is time to understand and respond to the needs and concerns of each individual, and when there are effective resources available for follow up if needed. Continuity of carer should be considered a key feature of routine trauma discussion, as many women will not disclose their histories to a stranger. All women should be provided with information about the impact of trauma and how to independently access support in the event of non-disclosures. Care providers need support to carry out these discussions.

Background The aim of this paper was to evaluate the change in 25-hidroxyvitamin D (25(OH)D) levels before and during the COVID-19 pandemic. Methods In this retrospective, cross-sectional and methodological study included 86,772 patients (18–75 years) samples who were admitted to the Izmir Dokuz Eylul University Hospital (latitude and longitude (Turkey): 27 E 09; 38 N 25, respectively) for various reasons and whose 25(OH)D levels were measured in the biochemistry unit between 2019–2020 and 2020–2021 (before and during the COVID-19 outbreak). A time series analysis of monthly averages for 25(OH)D was performed. For the purpose of seasonal study, the mean levels of 25(OH)D are grouped by years. Data were modeled in terms of 25(OH)D levels using the MATLAB Curve Fitting Toolbox. Results There was no significant difference between the sexes according to 25(OH)D levels (p>0.05). 25(OH)D levels were significantly higher in the summer months and lower in the winter months (p Conclusions Restrictions, partial or complete closures, and curfews can significantly affect individuals’ 25(OH)D levels during the COVID-19 outbreak. There is a need for multicenter studies with larger populations covering different regions to strengthen and support our results.

Using the data of the 2018 China Family Panel Studies (CFPS), this paper empirically tests the impact of the "Big Five" personality characteristics on household charitable donation behavior. The benchmark regression results show that after controlling the individual characteristics and family characteristics of the household heads, the conscientiousness and openness of the household heads have a significant positive impact on the social donation behavior of the family. On this basis, this paper takes the openness personality as an example, selects the identification strategy of processing effect, and tests the robustness of the causal effect of personality on household donation behavior. The openness personality has a significant positive impact on household external donation behavior. In the further study, it is found that with the improvement of the level of household charitable donation, the positive effect of the household head ’s openness personality on household charitable donation behavior is gradually weakening; The influence of openness personality on household charitable donation has the nonlinear characteristics of "marginal effect" increasing and obvious life cycle characteristics.

The logistics industry is closely related to the high-quality economic development. At different levels of industrial structure, the relationship between high-quality development of the logistics industry and the high-quality economic development will vary, resulting in different roles and paths in promoting economic development. However, there is still a lack of research on the relationship between high-quality development of the logistics industry and high-quality economic development at different levels of industrial structure, and further empirical research is needed. It used the benchmark regression model to analyze the impact of the high-quality development of the logistics industry on high-quality economic development, and the panel threshold model was used to analyze the impact of the logistics industry on high-quality economic development at different levels of industrial structure development. The results show that the high-quality development of the logistics industry has a positive role in promoting the high-quality economic development, and in different levels of industrial structure development, the impact of high-quality development level of logistics industry on the high-quality economic development is different. Therefore, it is necessary to further optimize the industrial structure, promote the deep integration and development of logistics and related industries, and continue to promote the high-quality development of the logistics industry. And when formulating development strategies for the logistics industry, governments and enterprises need to consider factors such as changes in industrial structure, the overall goals of national economy, people’s livelihood, and social development, in order to provide solid support for achieving high-quality economic development. This paper demonstrates the importance of high-quality development of the logistics industry in high-quality economic development, and it encourages the adoption of different strategies at different stages of industrial structure development to promote high-quality development of the logistics industry, and achieve high-quality economic development.

Previous research reports a positive association between possession of mental toughness (MT) and high performance in sportspersons. However, the extent to which MT is related to playing experiences and appreciation of club environment in elite women’s football has received only limited research attention. Accordingly, the present study investigated MT in the context of the English Football Association Women’s Super League (WSL). Specifically, this paper examined relationships between level of MT and external (playing experience, perceptions of club infrastructure, and appreciation of support mechanisms) and internal (self-esteem) factors. A sample of 63 elite female professional football players from the WSL, aged between 18 and 35 years (mean = 25.87, SD = 4.03), completed self-report measures. To objectively validate self-ratings, congruence between self and peer-rated was assessed. This revealed a strong degree of consistency. Subsequent analysis found positive correlations between MT, playing experience (number of years playing football, NoY; and highest level of football achieved, HLA), and External Support. Additionally, Self-Esteem correlated positively with MT, NoY, HLA, and External Support. Moderation analysis found MT interacted with NoY and predicted greater levels of Self-Esteem. Players with lower and mean MT, and more years as a professional were more likely to possess higher Self-Esteem (vs. less years). These outcomes indicated important relationships between MT, External Support, and Self-Esteem. Accordingly, WSL clubs can potentially apply the results of this study to enhance positive player mindset.

Sleep is an important indicator of a person’s health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person’s sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures—both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

As a major energy sources province in China, Shaanxi Province ranks top three in terms of raw coal production in China and undertakes the important task of ensuring national energy supply and security. Affected by the endowment of energy resources, fossil energy accounts for a large proportion of the energy consumption structure in Shaanxi Province, and it will face huge challenges under the severe carbon emission situation in future. In order to analyze the relationship between energy consumption structure, energy efficiency and carbon emissions, the paper introduces the concept of biodiversity into the energy industry. Taking Shaanxi Province as an example, the paper calculates the energy consumption structure diversity index and analyzes the impact of energy consumption structure diversity on energy efficiency and carbon emissions in Shaanxi Province. The results shows that the diversity index and equilibrium index of energy consumption structure in Shaanxi exhibits a slow upward trend in general. In most years, energy consumption structure diversity index in Shaanxi is higher than 0.8, and the equilibrium index is higher than 0.6. The carbon emissions of energy consumption in Shaanxi generally show increasing trend, and the carbon emissions have increased from 5,064.6 tons to 21899.67 tons from 2000 to 2020. The paper also shows that Shaanxi H index is negatively correlated with total factor energy utilization efficiency in Shaanxi, and positively correlated with carbon emissions in Shaanxi. The main reason is the internal substitution of fossil energy, and the proportion of primary electricity and other energy sources is still relatively low, which leads to a higher level of carbon emissions.

In order to overcome the discreteness of input data and training data in deep neural network (DNN), the multivariable response surface function was used to revise input data and training data in this paper. The loss function based on the data on the response surface was derived, DNN based on multivariable response surface function (MRSF-DNN) was established. MRSF-DNN model of recycled brick aggregate concrete compressive strength was established, in which coarse aggregate volume content, fine aggregate volume content and water cement ratio are influencing factors. Furthermore, the predictive analysis and extended analysis of MRSF-DNN model were carried out. The results show that: MRSF-DNN model had high prediction accuracy, the correlation coefficient between the real values and the forecast values was 0.9882, the relative error was between -0.5% and 1%. Furthermore, MRSF-DNN had more stable prediction ability and stronger generalization ability than DNN.

To assess the feeding practices and behaviors of women and young children participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), USDA currently funds the longitudinal WIC Infant and Toddler Feeding Practices Study-2 (WIC ITFPS-2). In 2013, the study used time-location sampling (TLS) to enroll a cohort of infants who participated in WIC around birth. The children are subsequently followed across their first six years of life, regardless of their participation in WIC, with an additional follow-up at age nine years. A woman may enroll her child in WIC either during pregnancy or postpartum. For this study, a representative sample of infants enrolled in WIC was desired. Because the associations between WIC prenatal support and education and feeding practices and behaviors are substantively important to this study, the sample needed to include both women enrolling their children prenatally and women enrolling their children postnatally. For prenatal WIC enrollees, we attempted to complete a prenatal interview with the mother prior to the child’s birth. This paper describes the TLS approach used and the challenges addressed in implementation of the sample design and selection for the WIC ITFPS-2. Our approach generated a probability sample (subject to site geographic and size exclusions) using a stratified, multistage design, but there were challenges at each stage of selection. First, a WIC site was selected, and then newly enrolled WIC participants were sampled within selected sites during predetermined recruitment windows based on the site’s average flow of new WIC enrollees. We discuss issues faced, including overcoming incomplete lists of individual WIC sites and discrepancies between projected new WIC enrollment counts and actual flow of new WIC enrollments during the recruitment period.

The service-oriented digital transformation of the construction industry is a development trend of cross-border industrial integration and transformation and upgrading in the digital economy environment, and collaborative value creation among stakeholders is seen as a strategic imperative to promote this process. This study aims to achieve efficient collaborative value co-creation and accelerate the digital transformation process of the construction industry by exploring the collaborative strategies and evolution laws of value co-creators in the digital service ecosystem of the construction industry. Based on evolutionary game theory and methods, this paper analyzes the evolutionary stability strategies and conditions of each participant in the service-oriented value chain at different stages of the digital transformation of the construction industry. It is found that with the improvement of the level of digitalization, the degree of cooperation among game players continues to increase until a stable state of full cooperation is achieved. The initial willingness of the game players to cooperate accelerates the speed of the system’s evolution to the stable state of full cooperation in the middle stage of digital transformation. Additionally, the improvement of the construction process digitalization level can subvert the evolution result of full non-coordination caused by a low initial willingness to cooperate. The research conclusions and corresponding countermeasures and suggestions can provide a strategic reference for the service-oriented digital transformation of the construction industry.

Background Celiac disease (CD) is an autoimmune disease affecting around 1.4% of the total human population. Local and systemic manifestations are described in CD. Viral infections seem to trigger CD or even have a worse outcome in CD patients. The evidence on the relationship between CD and coronavirus disease (COVID-19) is limited. To evaluate existing evidence on the association between CD and COVID-19, we conducted the current systematic review. Methods We systematically searched Pubmed, Scopus, and Embase databases to find articles that reported risks or outcomes of COVID-19 in CD patients. Papers in any language published up to November 17, 2022, were evaluated for possible inclusion. The results were analyzed qualitatively. This study is registered with PROSPERO(CRD42022327380). Results We identified 509 studies by searching databases; 14 reported data on the risk or outcome of COVID-19 in CD patients and were eligible for qualitative synthesis. We found that the relative risk of acquiring COVID-19 in CD patients may be lower than in the general population. Approximately 90% of infected patients were treated as an outpatient, and 10% were hospitalized. GFD adherence and Health-related quality of life (HR-QOL) were more or less the same before and during the pandemic. The gluten-free products (GFP) supply seems to be plunged during the pandemic. The data on the psychological effects of the pandemic were conflicting. Conclusion The risk of acquiring COVID-19 in CD patients is lower than in the general population. Females were more likely to be infected by COVID-19, and the most common comorbidity in infected patients was a chronic lower respiratory disease; around 10% of infected patients needed hospitalization, GFD adherence, and HR-QOL was more or less the same before and during the pandemic, depression, anxiety, and stress levels of patients varied among studies. Patients had more difficulties accessing GFPs based on limited data.

The dominant spatial econometric model in spatial econometrics is the parametric form, while in the realistic context, the variables often do not satisfy the assumption of linearity and have nonlinear relationships with each other. In this paper, we introduce nonparametric terms into spatial econometric models and propose the MCMCINLA estimation method for varying coefficient spatial lag models. The empirical analysis is conducted with the socioeconomic data of mainland China from 2015 to 2020 to discuss the influencing factors and spatial and temporal distribution characteristics of China’s economic development under the classical spatial lag model and the varying coefficient spatial lag model with population aging as a special covariate, respectively. The results show that with the gradual aging of the population, foreign trade will inhibit the development of regional economy to a certain extent, while urbanization process, resident income, real estate development and high-tech development will have a driving effect on economic growth, and high-tech development has the strongest mobilization on regional economic development. Compared with the classical spatial lag model, the varying coefficient spatial lag model can more fully exploit the information of variables in a more realistic context and derive the variable evolution process.

This paper proposes the conceptual design method for a hybrid-actuated lower limb exoskeleton based on energy consumption simulation. Firstly, the human-machine coupling model is established in OpenSim based on the proposed three passive assistance schemes. On this basis, the method of simulating muscle driving is used to find out the scheme that can reduce the metabolic rate the most with 3 passive springs models. Then, an active-passive cooperative control strategy is designed based on the finite state machine to coordinate the operation of the power mechanism and the passive energy storage structure and improve the mobility of the wearer. In the end, a simulation experiment based on the human-machine coupled model with the addition of active actuation is proceeded to evaluate its assistance performance according to reducing metabolic rate. The results show that the average metabolic cost decreased by 7.2% with both spring and motor. The combination of passive energy storage structures with active actuators to help the wearer overcome the additional consumption of energy storage can further reduce the body’s metabolic rate. The proposed conceptual design method can also be utilized to implement the rapid design of a hybrid-actuated lower limb exoskeleton.

The diffusion phenomena taking place in complex networks are usually modelled as diffusion process, such as the diffusion of diseases, rumors and viruses. Identification of diffusion source is crucial for developing strategies to control these harmful diffusion processes. At present, accurately identifying the diffusion source is still an opening challenge. In this paper, we define a kind of diffusion characteristics that is composed of the diffusion direction and time information of observers, and propose a neural networks based diffusion characteristics classification framework (NN-DCCF) to identify the source. The NN-DCCF contains three stages. First, the diffusion characteristics are utilized to construct network snapshot feature. Then, a graph LSTM auto-encoder is proposed to convert the network snapshot feature into low-dimension representation vectors. Further, a source classification neural network is proposed to identify the diffusion source by classifying the representation vectors. With NN-DCCF, the identification of diffusion source is converted into a classification problem. Experiments are performed on a series of synthetic and real networks. The results show that the NN-DCCF is feasible and effective in accurately identifying the diffusion source.