Identification of key factors influencing unsafe behavior of decoration workers.

This study explores the factors influencing unsafe behaviors among decorators. The study begins with a literature review on unsafe behaviors among construction workers, compiling a checklist of factors specific to decorators. Utilizing exploratory factor analysis (EFA), a measurement scale for these factors is developed. Subsequently, stepwise regression analysis (SRA) is conducted to validate relationships and identify crucial factors. Results categorize influencing factors into three dimensions: personal, organizational and environmental. Non-compliance with safety procedures and protocols is found to correlate directly with increased unsafe behavior at an individual level. Additionally, internal safety regulations within companies are identified as having a direct negative impact on unsafe behaviors at the organizational level. This study enhances our understanding of unsafe behaviors among decorators and offers recommendations for mitigation.

Factor mismatch measurement of resource-based cities: Evidence from all-round optimization total factor productivity decomposition framework.

In-depth analysis of the factors affecting the transformation of resource-based cities can provide effective support for the transformation and development of resource-dependent regions. How to comprehensively identify the factors affecting the transformation of resource-based cities is a complex problem. This study starts from the total factor productivity model and focuses on the two core basic factors that affect the transformation process of cities reliant on resources. Economic benefits and energy efficiency, respectively, from the economic benefit analysis framework and energy efficiency analysis framework for reconstruction, the two frameworks are combined with the use of distorted prices of resource elements to solve the problem that the synergistic effect of economic benefits and energy efficiency can not be measured. In order to quantitatively analyze the factors that affect the development efficiency of cities reliant on resources under the single or synergistic effect of the comprehensive framework, this study optimizes the directional distance function from three perspectives: exogenous weight, direction vector endogeneity, and absolute distance transformation relative distance, thus achieving an accurate assessment transformation efficiency of cities reliant on resources. Considering the impact of the new coronavirus epidemic, this study only selected the data of resource-based cities from 2003 to 2018, and found through model calculation that the impact on the transformation of cities reliant on resources: (1) Labor mismatch is mainly achieved by affecting the structure about the production of resource-based enterprises and industrial human resources; (2) Capital mismatch is mainly realized by affecting the production of resource-based enterprises; (3) Energy mismatch is mainly achieved by affecting high energy consumption enterprises and low production technology level enterprises. Further research shows that the main objects of these factors are the four parts of production technology level, energy consumption, total factor productivity and industrial structure. Through these contents, they affect environmental efficiency and deeply affect the transformation process of resource-based cities.

Promoting the development of ethnic medicine in China: policy evaluation and optimization countermeasures.

Background: The coordinated development of ethnic medicine is a basic necessity for steady construction of a healthy China. This process includes closely following domestic and foreign policies, including changes, through the optimization of policies; shaping the new direction of the development of national medicine; and achieving comprehensive technological and industrial upgrades. As such, ensuring the all-round development of national medicine in China remains a great challenge. Methods: This paper takes the relevant policies of national and local ethnic medicine issued by the government as the research object, and, through the full interpretation of the policy-issuing body, policy content, and policy effectiveness, deeply analyzes the current situation of the policy's role in ethnic medicine and explores the distribution of policy types, subject-cooperation modes, and scoring levels in various dimensions. Results: This study found that, in the new era of pharmaceutical reform, the State lacks a variety of special policies on ethnomedicine, and there is also an imbalance in the use of policy tools at both the central and local levels as well as synergies in the implementation of policies that need to be further strengthened. Discussion: There remains a need to continue to improve the policy-evaluation system, optimize the structure for the use of policy tools, and improve the rates of application and implementation of the national medicine policy by strengthening cross-provincial and multisectoral cooperation to promote the revitalization of the national medicine industry in China.

Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data.

BACKGROUND: In recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis. RESULTS: We proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise. CONCLUSION: Shaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud , and its source code is publicly available at https://github.com/WiedenWei/shaoxia .

Utilizing data platform management to implement "5W" analysis framework for preventing and controlling corruption in grassroots government.

In the era of information technology advancement, big data analysis has emerged as a crucial tool for government governance. Despite this, corruption remains a challenge at the grass-roots level, primarily attributed to information asymmetry. To enhance the efficacy of corruption prevention and control in grass-roots government, this study introduces the concept of data platform management and integrates it with the "5W" (Who, What, When, Where, Why) analysis framework. The research is motivated by the observation that existing studies on corruption prevention primarily concentrate on the formulation of laws and regulations, neglecting the potential improvement in actual effectiveness through the utilization of data platforms and analytical frameworks. The research employs methodologies grounded in the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis framework, the Plan, Do, Check, Act (PDCA) cycle analysis framework, and the 5W analysis framework. Throughout the iterative process of implementing data platform management, various timeframes are established, and the impact of the three models is evaluated using indicators such as public participation and government satisfaction. The research reveals that the SWOT framework can formulate targeted strategies, the PDCA framework continuously optimizes work processes, and the 5W framework profoundly explores the root causes of corruption. The outcomes indicate a 10.76% increase in the public participation level score with the 5W model, rising from 71.67%, and a 23.24% increase in the governance efficiency score, reaching 66.12%. The SWOT model excels in case handling prescription and corruption reporting rate. The synergistic application of the three models demonstrates a positive impact. In conclusion, the amalgamation of data platform management and a multi-model approach effectively enhances the corruption prevention capabilities of grass-roots governments, offering insights for the establishment of transparent and efficient grass-roots governance.

Design and hierarchical analysis of magnetic actuated robot: A governing equation based approach.

As the alternative solution to the conventional guidewire, the magnetic robot can help interventionists perform percutaneous coronary intervention (PCI) because magnetic fields are transparent and safe for biological tissues. Despite extensive research on magnetic robots, the exploration of their deflection control for practical applications still requires further research. In this paper, a hierarchical analysis framework (HAF) is proposed to control the magnetic robot's deflection. Six deflection subpatterns are analyzed through HAF, incorporating statistical and regression analyses to establish governing equations of magnetic robots. The performance of the control equations is validated through precise control of the magnetic continuum robot (MCR) and magnet-tipped robot (MTR) in both uniform and gradient magnetic fields. Experimental results show that under the uniform magnetic field, the average root mean square error (RMSE) of governing equation of MCR is 0.08±0.05°, 0.41±0.34°, 1.47±0.49° and 1.07±0.66° for four-types horizontal deflection, 0.19±0.07mm and 0.16±0.10mm for two-types vertical deflection, respectively. Based on the governing equations, the MTR is able to precisely navigate to coronary arteries with various degrees of stenosis (30%, 52%, and 60%), and successfully pass through a series of rings, with an average error of 1.05 mm. The research successfully demonstrates the potential of HAF in creating robust and reliable governing equations for magnetic actuation in medical robotics, with significant implications for enhancing the precision and safety of PCI procedures.

Research on the evaluation and configuration path of China's rural common Prosperity-NCA and fsQCA based on provincial panel data.

Based on 2013-2019 panel data covering 31 provinces and cities in China, this paper uses the entropy weight technique for order of preference by similarity to ideal solution (TOPSIS) method to measure rural common prosperity (CP). Based on the global and local Moran's I methods, we analyze the dynamic evolutionary characteristics of China's rural CP and the regional differences. Additionally, we use the necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) methods to explore how six antecedents at the technology-organization-environment (TOE) level interact to affect CP. This research finds that, first, China's rural CP showed a fluctuating upward trend, with the highest level of CP in rural areas in the eastern region, followed by the central and western regions. Additionally, the gap between the three gradually narrowed. Second, China's rural CP had "high-high" and "low-low" agglomeration characteristics, with positive spatial autocorrelation, no transition changes, and strong spatial stability. Third, individual digital economic elements and organizational and environmental elements were not necessary conditions for promoting rural CP. Fourth, the multiple concurrent factors of the digital economy, organizations and the environment constituted three diversified configurations of rural CP, showing that the driving path of rural CP was characterized by "different paths that lead to the same goal". Moreover, "perfect digital facilities" and "high entrepreneurial activity" had a universal role in promoting rural CP. The conclusions of this research hold important theoretical and practical significance for improving China's rural CP.

Age-dependent effects of oxytocin in brain regions enriched with oxytocin receptors.

Although intranasal oxytocin administration to tap into central functions is the most commonly used non-invasive means for exploring oxytocin's role in human cognition and behavior, the way by which intranasal oxytocin acts on the brain is not yet fully understood. Recent research suggests that brain regions densely populated with oxytocin receptors may play a central role in intranasal oxytocin's action mechanisms in the brain. In particular, intranasal oxytocin may act directly on (subcortical) regions rich in oxytocin receptors via binding to these receptors while only indirectly affecting other (cortical) regions via their neural connections to oxytocin receptor-enriched regions. Aligned with this notion, the current study adopted a novel approach to test 1) whether the connections between oxytocin receptor-enriched regions (i.e., the thalamus, pallidum, caudate nucleus, putamen, and olfactory bulbs) and other regions in the brain were responsive to intranasal oxytocin administration, and 2) whether oxytocin-induced effects varied as a function of age. Forty-six young (24.96 ± 3.06 years) and 44 older (69.89 ± 2.99 years) participants were randomized, in a double-blind procedure, to self-administer either intranasal oxytocin or placebo before resting-state fMRI. Results supported age-dependency in the effects of intranasal oxytocin administration on connectivity between oxytocin receptor-enriched regions and other regions in the brain. Specifically, compared to placebo, oxytocin decreased both connectivity density and connectivity strength of the thalamus for young participants while it increased connectivity density and connectivity strength of the caudate for older participants. These findings inform the mechanisms underlying the effects of exogenous oxytocin on brain function and highlight the importance of age in these processes.

A quantitative analysis framework of placenta accreta spectrum: placenta subtype, intraoperative bleeding, and hysterectomy risk evaluation based on magnetic resonance imaging-anatomical-clinical features.

Background: Placenta accreta spectrum (PAS) is a significant contributor to maternal morbidity and mortality. Our objective was to develop a quantitative analysis framework utilizing magnetic resonance imaging (MRI)-anatomical-clinical features to predict 3 clinically significant parameters in patients with PAS: placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy). Methods: A total of 125 pregnant women with PAS from 2 medical centers were enrolled into an internal training set and an external testing set. Some 21 MRI-anatomical-clinical features were integrated as input into the framework. The proposed quantitative analytic framework contains mainly 3 classifiers built by extreme gradient boosting (XGBoost) and their testing in external datasets. We also further compared the accuracy of placenta subtype prediction between the proposed model and 4 radiologists. A quantitative model interpretation method called SHapley Additive exPlanations (SHAP) was conducted to explore the contribution of each feature. Results: The placenta subtype (invasive vs. non-invasive), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) demonstrated impressive area under the receiver operating characteristic curve (AUROC) values of 0.93, 0.88, and 0.90, respectively, in the internal validation set. Even in the external testing set, these metrics maintained their strength, achieving AUROC values of 0.91, 0.82, and 0.82, respectively. Comparing our proposed framework to the 4 radiologists, our model exhibited superior accuracy, specificity, and sensitivity in predicting placental subtypes within the external testing cohort. The features associated with intraplacental dark T2 bands played a crucial role in the decision-making process of all 3 prediction models. Conclusions: The quantitative analysis framework can provide a robust method for classification of placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) based on MRI-anatomical-clinical features in PAS.

Evaluation of a data-driven intelligent waste classification system for scientific management of garbage recycling in a Chinese community.

Waste classification management is effective in addressing the increasing waste output and continuous deterioration of environmental conditions. The waste classification behaviour of resident is an important basis for managers to collect and allocate resources. Traditional analysis methods, such as questionnaire, have limitations considering the complexity of individual behaviour. An intelligent waste classification system (IWCS) was applied and studied in a community for 1 year. Time-based data analysis framework was constructed to describe the residents' waste sorting behaviour and evaluate the IWCS. The results showed that residents preferred to use face recognition than other modes of identification. The ratio of waste delivery frequency was 18.34% in the morning and 81.66% in the evening, respectively. The optimal time windows of disposing wastes were from 6:55 to 9:05 in the morning and from 18:05 to 20:55 in the evening which can avoid crowding. The percentage of accuracy of waste disposal increased gradually in a year. The amount of waste disposal was largest on every Sunday. The average accuracy was more than 94% based on monthly data, but the number of participating residents decreased gradually. Therefore, the study demonstrates that IWCS is a potential platform for increasing the accuracy and efficiency of waste disposal and can promote regulations implementation.

Construction of enterprise business management analysis framework based on big data technology.

With the development of science and technology, people have added a new concept big data, which is the most concerned topic at present, and has also brought great changes to the business management environment of enterprises. At present, most of the business administration work of enterprises is mainly based on human resources, and the enterprise activities are managed through the professional knowledge of relevant management personnel. However, due to human subjective factors, the management effect is unstable. Therefore, this paper designed an enterprise business management system based on intelligent data technology, and constructs an enterprise business management analysis framework. The system can help managers to make the best plan when implementing management measures, improve the efficiency of production management, sales management, financial management, personnel organization structure management, etc., so as to make business management more scientific. The experimental results showed that the improved C4.5 algorithm in the business management system proposed in this paper reduced the fuel consumption cost of shipping company A by 220.21 yuan at least and 11050.12 yuan at most, which reduced the fuel consumption cost of the company's five voyages by 13349.09 yuan in total. This indicates that the improved C4.5 algorithm has higher accuracy and better time efficiency compared to traditional C4.5 algorithms. At the same time, the optimized ship speed management effectively reduces the fuel consumption cost of flights and improves the company's operating profit. The article proves the feasibility of improved algorithms based on decision trees in enterprise business management systems, and has a good decision support effect.

An Innovative Tool for Evidence-Based, Personalized Treatment Trials in Mucopolysaccharidosis.

Mucopolysaccharidosis (MPS) is a group of rare metabolic diseases associated with reduced life expectancy and a substantial unmet medical need. Immunomodulatory drugs could be a relevant treatment approach for MPS patients, although they are not licensed for this population. Therefore, we aim to provide evidence justifying fast access to innovative individual treatment trials (ITTs) with immunomodulators and a high-quality evaluation of drug effects by implementing a risk-benefit model for MPS. The iterative methodology of our developed decision analysis framework (DAF) consists of the following steps: (i) a comprehensive literature analysis on promising treatment targets and immunomodulators for MPS; (ii) a quantitative risk-benefit assessment (RBA) of selected molecules; and (iii) allocation phenotypic profiles and a quantitative assessment. These steps allow for the personalized use of the model and are in accordance with expert and patient representatives. The following four promising immunomodulators were identified: adalimumab, abatacept, anakinra, and cladribine. An improvement in mobility is most likely with adalimumab, while anakinra might be the treatment of choice for patients with neurocognitive involvement. Nevertheless, a RBA should always be completed on an individual basis. Our evidence-based DAF model for ITTs directly addresses the substantial unmet medical need in MPS and characterizes a first approach toward precision medicine with immunomodulatory drugs.

Individual Treatment Trials-Do Experts Know and Use This Option to Improve the Treatability of Mucopolysaccharidosis?

Mucopolysaccharidoses (MPS) are a group of rare, heterogeneous, lysosomal storage disorders. Patients show a broad spectrum of clinical features with a substantial unmet medical need. Individual treatment trials (ITTs) might be a valid, time- and cost-efficient way to facilitate personalized medicine in the sense of drug repurposing in MPS. However, this treatment option has so far hardly been used-at least hardly been reported or published. Therefore, we aimed to investigate the awareness and utilization of ITTs among MPS clinicians, as well as the potential challenges and innovative approaches to overcome key hurdles, by using an international expert survey on ITTs, namely, ESITT. Although 74% (20/27) were familiar with the concept of ITTs, only 37% (10/27) ever used it, and subsequently only 15% (2/16) published their results. The indicated hurdles of ITTs in MPS were mainly the lack of time and know-how. An evidence-based tool, which provides resources and expertise needed for high-quality ITTs, was highly appreciated by the vast majority (89%; 23/26). The ESITT highlights a serious deficiency of ITT implementation in MPS-a promising option to improve its treatability. Furthermore, we discuss the challenges and innovative approaches to overcome key barriers to ITTs in MPS.

RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework.

We present RNASequest, a customizable RNA sequencing (RNAseq) analysis, app management, and result publishing framework. Its three-in-one RNAseq data analysis ecosystem consists of (1) a reproducible, configurable expression analysis (EA) module, (2) multi-faceted result presentation in R Shiny, a Bookdown document and an online slide deck, and (3) a centralized data management system. In principle, following up our well-received omics data visualization tool Quickomics, RNASequest automates the differential gene expression analysis step, eases statistical model design by built-in covariates testing module, and further provides a web-based tool, ShinyOne, to manage apps powered by Quickomics and reports generated by running the pipeline on multiple projects in one place. Researchers can experience the functionalities by exploring demo data sets hosted at http://shinyone.bxgenomics.com or following the tutorial, https://interactivereport.github.io/RNASequest/tutorial/docs/introduction.html to set up the framework locally to process private RNAseq datasets. The source code released under MIT open-source license is provided at https://github.com/interactivereport/RNASequest.

Comparative Analysis of Long-Term Care in OECD Countries: Focusing on Long-Term Care Financing Type.

This study aims to examine the characteristics of long-term care (LTC) financing in Organization for Economic Cooperation Development (OECD) countries. To this end, the 26 OECD countries that have introduced LTC systems were classified into three types of models: tax-based, health insurance, and LTC insurance (LTCI) systems. Thereafter, these systems were analyzed using Gilbert and Terrell's policy analysis framework. The results indicated differences in the characteristics of each type of financing in terms of allocation, benefit provision, service delivery, and finance. It is likely that while the LTC insurance model was mainly based on universalism and showed the highest level of coverage, the tax-based and health insurance models adopted selectivism with lower level of benefits per capita. In terms of service delivery, local authorities tended to have the responsibility to decide LTC service users and provide services in many countries, regardless of the type of model. In terms of finance, LTC insurance-based countries had the highest LTC expenditure as a percentage of GDP, followed by countries with tax-based and health insurance systems.

Assessing Chinese governance low-carbon economic peer effects in local government and under sustainable environmental regulation.

The current study developed a systematic analytical framework to explore the logic of forming the cohort effect of green governance and green development in China in the new era. Based on provincial panel data from 2008 to 2018, this paper examines the existence, scope, and induced control of the green governance peer effects using a spatial econometric approach. The study found that the following: (1) Influenced by the top-level design of the central government and the contradictory governance of regional development, the local governments form the peer effects in green governance activities. The existence of spatial relationships makes local governments dependent on a solid financial support system and a basis for industrial transformation, thus counteracting regional competition for green governance. (2) The green governance peer effects tend to decay with increasing geographical distance but do not disappear across regional boundaries under either spatial interaction framework. (3) Considering the impact of green governance policy systems and regional heterogeneity, the green governance peer effects decrease in the eastern, western, and central regions in that order. (4) Further, the influencing factors show that the green governance peer effects arise from intra-local government competition under the decentralization of power between the central and local governments. The competition for scales and the relative performance appraisal system reinforces the peer motivation of each subject. (5) The strong correlation of green governance willingness indicates that local governments cannot escape from will-led emotional behavior, and personal interests and governance motivation further drive the formation of pseudo-rational decisions, ultimately leading to irrational group decisions.

WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures.

With the increase in massive digitized datasets of cultural artefacts, social and cultural scientists have an unprecedented opportunity for the discovery and expansion of cultural theory. The WikiArt dataset is one such example, with over 250,000 high quality images of historically significant artworks by over 3000 artists, ranging from the 15th century to the present day; it is a rich source for the potential mining of patterns and differences among artists, genres, and styles. However, such datasets are often difficult to analyse and use for answering complex questions of cultural evolution and divergence because of their raw formats as image files, which are represented as multi-dimensional tensors/matrices. Recent developments in machine learning, multi-modal data analysis and image processing, however, open the door for us to create representations of images that extract important, domain-specific features from images. Art historians have long emphasised the importance of art style, and the colors used in art, as ways to characterise and retrieve art across genre, style, and artist. In this paper, we release a massive vector-based dataset of paintings (WikiArtVectors), with style representations and color distributions, which provides cultural and social scientists with a framework and database to explore relationships across these two vital dimensions. We use state-of-the-art deep learning and human perceptual color distributions to extract the representations for each painting, and aggregate them across artist, style, and genre. These vector representations and distributions can then be used in tandem with information-theoretic and distance metrics to identify large-scale patterns across art style, genre, and artist. We demonstrate the consistency of these vectors, and provide early explorations, while detailing future work and directions. All of our data and code is publicly available on GitHub.

Health Equity in Climate Change Policies and Public Health Policies Related to Climate Change: Protocol for a Systematic Review.

The relationship between risks to health equity and climate change is well established, and various international organizations and literature has called for intersectoral action for tackling these issues. While there is a growing recognition of the importance of equity-focused responses to climate change, if and how health equity measures are incorporated into climate-change-related policy decisions has not yet been adequately explored. There are numerous approaches and frameworks for conducting policy analysis, and for understanding equity in the context of climate change and public health separately; however, a comprehensive framework for exploring the complexity of these interacting factors is hard to find. This review aims to systematically identify policy analysis frameworks and studies focusing on health equity in climate change related policies. Five electronic databases will be searched for peer-reviewed articles in English and from 2000. Articles will be subjected to systematic retrieval and quality assessment, and thematic analysis will be used for data analysis. The study findings will provide insight into different existing policy-analysis frameworks and policy-analysis approaches to understand health equity considerations in climate change policies and in health policies related to climate change.