Disposal of unlawful interference incidents is essential for is crucial for the advancement of aviation security. Effective emergency disposal requires a comprehensive approach that includes the perspectives of airlines, airports, and passengers. In this context, each component of the disposal process can fail randomly. The objective of this research is to optimize emergency disposal decisions to enhance the efficiency of civil aviation operations, reduce accidents, and lower costs. Given the dynamic complexity of unlawful interference incidents, a dynamic fault tree consisting of 26 nodes was constructed to analyze the emergency disposal process. To explore the relationships and priorities of each event, the Dynamic Fault Tree is converted into a dynamic Bayesian network. Based on historical statistical data, simulation analysis is conducted in three aspects: posterior probability, sensitivity, and importance. Simulation results reveal that the top three critical nodes in cabin unlawful interference incidents are \"structural damage to the cabin,\" \"inadequate training by airlines,\" and \"untimely airport police takeover of disruptive passengers.\" Further analysis shows that (1) most of the critical nodes are associated with airlines. (2) The decision-making rationale and pathways of the critical nodes can be clearly observed and prioritized. (3) Besides airlines, other entities such as airports can implement targeted emergency disposal measures. Through quantitative analysis and simulation, this study provides decision-making guidance for participating groups on dynamic emergency disposal, thereby enhancing civil aviation security.

Historic districts are integral components of urban space, possessing diverse ecosystems that can offer various cultural services to the public. Urbanization and tourism development have led to the degradation of the ecological landscapes within historic districts, impacting sustainable development. Incorporating Cultural Ecosystem Services (CES) into the environmental research of historic districts can meet people\'s spiritual needs, enhance intangible benefits for humanity, and promote the conservation of the ecological environment within historic districts. Therefore, this study conducted perceptual quantification research on CES in four typical historic districts in Fuzhou City, crawling the online comment data through Python, mined its potential themes using Biterm Topic Model (BTM), and extracted and categorized the indicators of CES of historic districts by combining with expert consultation; meanwhile, the satisfaction of CES of historic districts is further explored with the help of two methods, namely, sentiment analysis and Importance-Performance analysis (IPA), and summarized the public perception of CES of historic districts. The results of the study show that: (1) the dimensions of public perception of CES in urban historic districts include Cultural Heritage, Leisure Tourism, Aesthetic Enjoyment, Spiritual Fulfillment, Inspiration, and Science Education six indicators, of which Leisure Tourism is most easily perceived by the public, but its satisfaction is not high; (2) the public\'s perception of positive emotions towards the CES of historic districts in Fuzhou is greater than negative emotions, with positive emotions accounting for 80.61%; (3) the public\'s overall satisfaction with the CES of Fuzhou\'s historic districts is high, and according to the final analysis results of the IPA, the four historic districts of Fuzhou are respectively proposed to improve the opinions. Based on big data, this study explores the public perception characteristics of CES in Fuzhou historic districts to promote its sustainable development and improve public well-being, which is of great significance to protecting the ecological environment of historic districts and improving the quality of cultural services.

Anthropogenic influences significantly modify the hydrochemical properties and material flow in riverine ecosystems across Asia, potentially accounting for 40-50% of global emissions. Despite the pervasive impact on Asian rivers, there is a paucity of studies investigating their correlation with carbon dioxide (CO2) emissions. In this study, we computed the partial pressure of CO2 (pCO2) using the carbonate equilibria-based model (pCO2SYS) and examined its correlation with hydrochemical parameters from historical records at 91 stations spanning 2013-2021 in the Ganga River. The investigation unveiled substantial spatial heterogeneity in the pCO2 across the Ganga River. The pCO2 concentration varied from 1321.76 μatm, 1130.98 μatm, and 1174.33 μatm in the upper, middle, and lower stretch, respectively, with a mean of 1185.29 μatm. Interestingly, the upper stretch exhibited elevated mean pCO2 and FCO2 levels (fugacity of CO2: 3.63 gm2d-1) compared to the middle and lower stretch, underscoring the intricate interplay between hydrochemistry and CO2 dynamics. In the context of pCO2 fluctuations, nitrate concentrations in the upper segment and levels of biological oxygen demand (BOD) and dissolved oxygen (DO) in the middle and lower segments are emerging as crucial explanatory factors. Furthermore, regression tree (RT) and importance analyses pinpointed biochemical oxygen demand (BOD) as the paramount factor influencing pCO2 variations across the Ganga River (n = 91). A robust negative correlation between BOD and FCO2 was also observed. The distinct longitudinal patterns of both parameters may induce a negative correlation between BOD and pCO2. Therefore, comprehensive studies are necessitated to decipher the underlying mechanisms governing this relationship. The present insights are instrumental in comprehending the potential of CO2 emissions in the Ganga River and facilitating riverine restoration and management. Our findings underscore the significance of incorporating South Asian rivers in the evaluation of the global carbon budget.

The primary aim of this research was to propose algorithms enabling the identification of significant reactions and subprocesses within models of biological systems constructed using classical Petri nets. These solutions allow to performance of two analysis methods: an importance analysis for identifying individual reactions critical to the functioning of the model and an occurrence analysis for finding essential subprocesses. To demonstrate the utility of these methods, analyses of an example model have been performed. In this case, it was a model related to the DNA damage response mechanism. It is worth noting that the proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods represent an extension of classical Petri net-based analyses. Their utility lies in their potential to enhance our comprehension of the biological phenomena under investigation. Furthermore, they can lead to the development of more effective medical therapies, as they can aid in the identification of potential molecular targets for drugs.

The literature shows how sarcopenia often occurs along with different phenotypes based either on the concomitant presence of adipose tissue excess (i.e., sarcopenic obesity, SO), or osteopenia/osteoporosis (osteosarcopenia, OS), or the combination of the two conditions, so-called osteosarcopenic obesity (OSO). This research aimed to assess the prevalence of sarcopenia phenotypes (SO, OS, OSO), their associated risk factors and their health impact in a population of out- and inpatients living in the North of Italy. Male and female subjects aged ≥18 years were enrolled for the study. A blood sample was collected to measure targeted blood makers. A comprehensive anthropometric clinical assessment (height, weight, Body Mass Index, BMI and Dual Energy X-ray Absorptiometry, DXA) was performed to measure ponderal, bone, fat, and muscle status. A total of 1510 individuals participated to the study (females, n = 1100; 72.85%). Sarcopenia was the most prevalent phenotype (17%), followed by osteosarcopenia (14.7%) and sarcopenic obesity. Only 1.9% of the sample was affected by OSO. According to logistic regression analysis, sarcopenia was associated with age, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) (positively) and BMI, Iron (Fe), Total Cholesterol, albumin (%), albumin (g), and gamma proteins (negatively). Sarcopenic obesity was associated with age, ferritin, ESR, CRP (positively) and BMI, Fe, and albumin (%) (negatively). Osteosarcopenia was associated with age, ESR (positively) and BMI, Total Cholesterol, albumin (%), albumin (g), and Ca (negatively). Osteosarcopenic obesity was associated with glycemia and gamma-glutamyl transferase (gGT) (positively). According to random forest analysis, a higher BMI was the most important protective factor for sarcopenia, for sarcopenic obesity (along with Iron) and for osteosarcopenia (along with albumin). Moreover, osteosarcopenic obesity was positively associated with GgT and glycaemia. The possibility of gaining such information, especially in the younger population, could help to prevent the onset of such diseases and best fit the patient\'s needs, according to a precision-medicine approach.

Using a dataset encompassing 228 cities in China spanning from 2005 to 2019, this study explores the nonlinear relationship between air quality and housing prices and devises a strategy that incorporates the instrumental variable and machine learning to address the endogeneity issue. Both traditional models and machine learning models find air pollution affects housing prices in a diminishing manner. The negative impact of air pollution on housing prices decreases when the degree of air pollution intensifies. Such a characteristic is more pronounced in Eastern China and cities with fewer land resource constraints and larger populations. Mechanism analysis also reveals that air pollution could affect residents\' perceived air quality and the industrial structure, further contributing to the nonlinear relationship between air quality and housing prices. The further SHapley Additive exPlanations (SHAP) evaluates the importance of air quality in determining housing prices and finds that air quality\'s contribution outweighs educational and medical resources. The contribution of air quality also shows a distinct regional disparity and has become increasingly important in recent years. The findings refine the benefit assessment accuracy related to air quality improvement.

Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient\'s physical conditions.
All medical data and 69,375 dialysis records of 314 Asian patients undergoing hemodialysis at a single dialysis center in Japan between July 2018 and April 2020 were collected from the electronic medical record system. Using the random forest classifier, we developed models to predict the probabilities of adjusting the dry weight at each dialysis session.
The areas under the receiver-operating-characteristic curves of the models for adjusting the dry weight upward and downward were 0.70 and 0.74, respectively. The average probability of upward adjustment of the dry weight had sharp a peak around the actual change over time, while the average probability of downward adjustment of the dry weight formed a gradual peak. Feature importance analysis revealed that median blood pressure decline was a strong predictor for adjusting the dry weight upward. In contrast, elevated serum levels of C-reactive protein and hypoalbuminemia were important indicators for adjusting the dry weight downward.
The random forest classifier should provide a helpful guide to predict the optimal changes to the dry weight with relative accuracy and may be useful in clinical practice.

Significant changes are taking place in the structure of tourism participants. Due to the ageing of societies, the tourism sector has to respond to the increasing tourist activity of seniors. The main aim of our research was the recognition of the needs of senior tourists from selected regions of Poland, considering their health and financial situation as well as their physical activity. The study shows how to combine the knowledge of assumptions of active ageing with the actual views of senior tourists on tourism and active leisure. An additional objective was to determine the reasons why seniors gave up tourism and to compare the reasons why seniors from selected regions of Poland and seniors from other European countries did not participate in tourism. Based on Eurostat data, we identify the most common reasons for people not participating in tourism who are over 65 years of age. In 2020, we surveyed seniors. The respondents for the sample were selected as 65 years and older. In order to compare countries due to exclusion and non-participation of seniors in tourism, the results classification was used. To analyse the touristic behaviours of Polish seniors, we used correspondence analysis. As indicated by analysing the reasons for the non-participation of Europeans aged 65 and over in tourism, in most countries, financial and health reasons are ranked first or second in 2016 and 2019. In a survey of Polish seniors, except for the financial reasons responsible for non-participation in tourism, an additional obstacle was the language barrier in foreign tourism. The analysis of physical and tourist activity showed that non-participation in tourism is associated with low physical activity. Women reported that they were satisfied with their financial independence and most often used the opportunity of short-term tourism. The people who are fully or largely involved in organising their trips also willingly change their locations during their next travels.

This study aims to evaluate and monitor the efficacy of a full-scale two-stage multi-soil-layering (TS-MSL) plant in removing fecal contamination from domestic wastewater. The TS-MSL plant under investigation consisted of two units in series, one with a vertical flow regime (VF-MSL) and the other with a horizontal flow regime (HF-MSL). Furthermore, this study attempts to see whether linear model (LM) and K-nearest neighbor (KNN) model can be used to predict total coliform (TC) removal in the TS-MSL system. For 24 months, the TS-MSL system was monitored, with bimonthly measurements recorded at the inlet and outlet of each compartment. Obtained results show removal of 85% of COD, 67% of TP, 27% of TN, and 3 log units of coliforms with good system stability. Thus, the effluent meets the Moroccan water quality code for reuse in the irrigation of green spaces. In addition, as compared to LM, the KNN model (R2 = 0.988) may be considered as an effective method for predicting TC removal in the TS-MSL system. Finally, sensitivity analysis has shown that TC and dissolved oxygen level in the influent were the most influential parameters for predicting TC removal in the TS-MSL system.

A large number of studies have used electromyography (EMG) to measure the paraspinal muscle activity of adolescents with idiopathic scoliosis. However, investigations on the features of these muscles are very limited even though the information is useful for evaluating the effectiveness of various types of interventions, such as scoliosis-specific exercises. The aim of this cross-sectional study is to investigate the characteristics of participants with imbalanced muscle activity and the relationships among 13 features (physical features and EMG signal value). A total of 106 participants (69% with scoliosis; 78% female; 9-30 years old) are involved in this study. Their basic profile information is obtained, and the surface EMG signals of the upper trapezius, latissimus dorsi, and erector spinae (thoracic and erector spinae) lumbar muscles are tested in the static (sitting) and dynamic (prone extension position) conditions. Then, two machine learning approaches and an importance analysis are used. About 30% of the participants in this study find that balancing their paraspinal muscle activity during sitting is challenging. The most interesting finding is that the dynamic asymmetry of the erector spinae (lumbar) group of muscles is an important (third in importance) predictor of scoliosis aside from the angle of trunk rotation and height of the subject.