Main Influencing Variables Research Articles

The awareness of sharenting in Italy: a pilot study

BackgroundThis pilot study examines the widespread phenomenon of “sharenting” and how it is perceived by parents. Given the increasing prevalence of this practice, the study aims to explore parental awareness of its potential risks and assess whether it is perceived as a form of child abuse, particularly regarding the violation of children’s privacy. While traditional forms of child abuse inflict direct harm on the child, sharenting can damage the child’s image and pose risks to their well-being, both in the present and the future. We evaluated the potential correlation between specific demographic characteristics and parents’ social media usage. The study aimed to assess the possibility of expanding the research by involving European pediatric societies to obtain comparable data and develop family education programs with the goal of limiting this phenomenon. It also emphasizes the important role that pediatricians and schools can play in these programs.MethodsThe study was designed using a survey model, one of the recognized quantitative research methods described in the literature and was conducted by the Pediatric Department of the University Hospital Campus Bio-Medico in Rome between November 2023 and January 2024.ResultsData from this study indicate that gender, age, education level, number of children, and number of social media accounts were not associated with sharenting phenomenon and suggest that the frequency and the onset time of sharenting were the main influencing variables in the perception of sharenting phenomenon as neglect and abuse.ConclusionsPreventive interventions, such as counseling and parental education, are essential to safeguard children’s well-being and prioritize their best interests, including their privacy and identity. Moreover, while the data from this study are limited, they underscore the importance of expanding data collection efforts across different countries. Establishing a comprehensive database could be beneficial for local and European governments in developing policies and educational programs aimed at mitigating potential risks associated with the improper exploitation of personal data, thus safeguarding children in both the short and long term.

Short-term wind power prediction method based on multivariate signal decomposition and RIME optimization algorithm

Accurate characterization of the dynamics of wind power systems under highly unstable conditions is essential for short-term wind power forecasting. Multivariate factors, such as wind speed, wind direction and climatic factors at different heights, as well as the relationships between them, need to be fully considered. To this end, after the Pearson correlation coefficient is used to identify the main influencing variables, the empirical modal decomposition (MEMD) method is utilized to decompose the data in order to capture the deep coupling relationship and the spatio-temporal pattern of change among the variables. Combined with a bidirectional long and short-term memory network (BiLSTM) optimized through the RIME algorithm, a short-term wind power prediction model was constructed. Data analysis of two wind farms in Xinjiang and Gansu, China, shows that the model has higher prediction accuracy compared to the baseline model. The effectiveness of RIME and MEMD is verified through component ablation experiments and comparison experiments with other optimization algorithms and signal decomposition algorithms. To cope with large-scale data, dimensionality reduction of IMF features using Elastic Net (EN) is explored to reduce computational requirements and maintain good prediction performance. The method provides strong support for power system scheduling and efficient utilization of renewable energy.

Experimental Study on the Accumulation Characteristics and Submergence Degree of Three-Dimensional Granular Rock Landslides in Shallow-Water Areas

This study combined the characteristics of rock mass structure, channel characteristics of main and tributary rivers, and flow conditions in the Three Gorges Reservoir area of the Yangtze River and designed and constructed a three-dimensional granular rock shallow-water landslide surge model test system under dynamic water flow conditions. The main influencing variables, including the volume of the landslide body, the inclination of the sliding surface, the water depth of the channel, and the flow velocity, were determined. A four-factor four-level orthogonal test design and a single-factor test design were combined to determine 28 sets of test conditions. Based on existing field surveys and physical model test simulation results, the motion process of the landslide body was divided into four stages: starting, accelerating, slowly accelerating or decelerating, and rapidly decelerating. The variations in the motion and velocity of the landslide body over time were determined by analyzing the image sequence extracted using a high-speed camera. The morphological characteristics of the landslide accumulation body were analyzed, and the submerged rate of the landslide body was statistically calculated. The relationship between the maximum bottom width of the landslide underwater accumulation body and the volume of the underwater accumulation body was established.

CO2 emission reduction, loads analysis, and strategies for new healthcare facilities design – Application on a case study

To understand the “Zero Emission Building” goal introduced by the Energy Performance of Buildings Directive (EPBD) revision, it is certainly important to define its scope, since different considerations must be made for buildings with different uses, characteristics and locations. In this paper, the case study of a new construction hospital building is analysed, bringing attention to two main aspects: the correct identification of energy needs of the Heating, Ventilation and Air Conditioning (HVAC) systems; Mechanical, Electrical and Plumbing (MEP) design strategies aimed at minimizing CO2 emissions. To pursue these objectives, the hourly trends of energy consumption are examined, focusing on the interpretation of their distribution, the main influencing variables and on the distribution of such energy consumption among the different utilities of the analysed system. It is opinion of the authors that this in-depth study is preparatory to the proper MEP design of the systems: in fact, by knowing the most energyconsuming users and the distribution of heating and cooling loads during the year, it is possible to identify the optimal design strategies and generation technologies to reduce the consumption of Primary Non-Renewable Energy. In this regard, the choice of an ap-propriate calculation model is highlighted as crucial. Possible design strategies related to the case study under consideration are analysed in this paper, ultimately exposing the general considerations regarding the ZEB issue in the hospital setting.

Assessment of spring water quality of Khandbari Municipality in Sankhuwasabha District, Eastern Nepal.

The study was carried out in the Khandbari Municipality, Sankhuwasabha District, Eastern Nepal to document the spring location and assess the water quality of the spring water for drinking and irrigation purposes. A total of 85 springs were mapped, which are located from 274 to 2176 m in altitude. Spring water samples were collected from 33 springs in the pre-monsoon (November, 2021) and 31 springs in the post-monsoon (March, 2022). Correlation matrices, t-test, principal component analysis (PCA), Piper diagram, Gibbs diagram, water quality index (WQI), United States Salinity Laboratory (USSL) diagram, and Wilcox diagram were applied for evaluating the spring water. All the physicochemical parameters were within the Nepalese National Drinking Water Quality Standard (NDWQS) and drinking water quality guidelines of the World Health Organization (WHO) except for pH in the pre-monsoon and iron in the post-monsoon season. The main contributors to the groundwater are Na+, Ca2+, Cl-, total dissolved solids (TDS), and total hardness, which exhibit significant correlations with electrical conductivity (EC) similar to TDS, suggesting their common source of origin. Based on the WQI, spring water is excellent in the post-monsoon and excellent and good in the pre-monsoon season. Furthermore, the spring water is excellent for irrigation purposes except for thepercent sodium in the post-monsoon and the magnesium ratio in the pre-monsoon season. Gibbs diagram illustrates that spring water is mainly governed by rock and precipitation dominance in some springs. The PCA indicates that anthropogenic activities (mixing of human waste and agricultural run-off in the spring water) are the main causes of contamination. Piper trilinear diagram demonstrates carbonate dissolution and silicate weathering as major processes for controlling the spring water chemistry. The study reveals that 62.5% of spring water was contaminated with microbes. For benthic macroinvertebrates, 18 springs were sampled, where nine orders and 17 families were recorded in the pre-monsoon and six orders and ten families in the post-monsoon season. The main influencing variables for macroinvertebrate assemblages are elevation, discharge, NO3-, and NH3.

Habitat suitability evaluation of invasive plant species Datura stramonium in Liaoning Province: Based on Biomod2 combination model.

Datura stramonium, as a major invasive plant in Liaoning Province, is difficult to be removed after its successful invasion, and is a great threat to ecological environment and biodiversity. To evaluate the habitat suitabi-lity of D. stramonium, we collected its geographic distribution data in Liaoning Province through field investigation and database query, and using the Biomod2 combination model, and investigated its potential and suitable distribution areas and main influencing environmental variables at present and under future climate change scenarios, respectively. The results showed that the combined model which composed of GLM (generalized linear model), GBM (generalized boosting regression model), RF (random forest model), and MaxEnt (maximum entropy model) had a good performance. By classifying the habitat suitability of D. stramonium into four categories: high-, medium-, low- and un-suitable habitats, we found that the high-suitable habitats were generally distributed in the northwest and south of Liaoning Province, with an area of about 3.81×104 km2, accounting for 25.8% of the total area. The medium-suitable habitats were mostly distributed in the northwest and central parts of Liaoning Province, with an area of about 4.19×104 km2, accounting for 28.3% of the total area. Slope and clay content of topsoil (0-30 cm) were the two main variables explaining the habitat suitability of D. stramonium, and the total suitability of D. stramonium first increased and then decreased with the increasing slope and clay content of topsoil in this region. Under future climate change scenarios, the total suitability of D. stramonium showed an expanding trend, and its suitability would be obviously increased in Jinzhou, Panjin, Huludao, and Dandong.

CIN2 + detection in high-risk HPV patients with no or minor cervical cytologic abnormalities: a clinical approach validated by machine learning

PurposeTo evaluate the feasibility and diagnostic value of the combination of colposcopy, cytology and hrHPV (high-risk human papilloma virus) PCR (polymerase chain reaction) testing in patients with no or minor cytologic abnormalities and HPV high risk infection and to find the best predictors for the presence of CIN2 + in this patient collective.MethodsThree hundred and thirty-four hrHPV patients with normal cytology or minor cytologic abnormalities who had a colposcopic examination at the center of colposcopy at the university hospital Aachen in 2021 were enrolled in this retrospective cohort analysis. Multivariate logistic regression and a machine-learning technique (random forests, leave-one-out analysis) were used.ResultsThe overall risk for CIN2 + in hrHPV-positive patients with normal cytology was 7.7% (N = 18) (5% for CIN3 +), 18% (N = 16) (10.1% for CIN3 +) in patients with PAP IIp (ASC-US) and 62.5% (N = 5) (25% for CIN3 +) in patients with PAP IIg (AGC). Variables that show a statistically significant influence for the CIN-status are ‘major change’ as the result of colposcopy, transformation zone type T1, PAP IIg upon referral (AGC) and hrHPV category 1a (HPV 16/18) detection. Using machine learning (random forests) techniques, the main influencing variables were confirmed. A monotonously decreasing risk for CIN2 + from hrHPV category 1a to 3 (in accordance to the IACR guidelines) was found.ConclusionIn the collective of hrHPV patients with no or minor cytologic abnormalities, the result of colposcopy and HPV PCR status are key predictors for the detection of CIN2 + with a monotonously decreasing risk for CIN2 + from hrHPV category 1a to 3.

Improving the sediment and nutrient first-flush prediction and ranking its influencing factors: An integrated machine-learning framework

Pollutant first flush (FF) is a critical phenomenon in urban regions driven by multiple factors. Therefore, having a robust mathematical FF definition and modeling tool along with thorough knowledge of its main influencing variables, is essential. Machine-learning techniques have been widely exploited in the water-quality field; however , their use to predict FF occurrence and pollutant load is limited. This study developed a generic machine-learning framework based on the two most commonly adopted FF definitions (30/80 and M(V) curve) and implemented for three of the most detected pollutants in urban areas (TSS, TN, and TP). This framework is able to accurately predict if a rainfall event generates FF and, in case it does, the correspondent pollutant load (”very good” model performance). It can also quantify and rank the most influencing factors using a non-model-biased method based on game theory (Shapley Additive exPlanations (SHAP)), showing that the rainfall variables are the most significant predictors of sediment and nutrient FF occurrence in urban regions. The findings proved that the machine-learning framework developed in this study can be a valuable modeling tool for improving the accuracy and reliability of pollutant load prediction during FF events in urban areas. Furthermore, they represent one of the first steps towards a robust mathematical FF definition, which is still a topic under debate.

Lecturer Performance and its Determining Factors in a Blended Learning System During The COVID-19 Pandemic

This study aims to determine lecturer performance in the implementation of blended learning and analyzes the effect of the main influencing variables. The study was conducted at the State University of Medan. The results show that ICT capability has a positive effect on lecturer performance either directly or through learning materials and LMS. Learning materials significantly affect the performance of lecturers, but this is not the case with LMS. Learning planning has a significant effect on lecturer performance even though learning resource support is not significant. The main determinants of lecturer performance are leadership, learning planning, and learning materials.

Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China

Housing price is one of the most concerning issues to the public worldwide. Studying the spatial characteristics of Shanghai’s housing prices and their explanatory factors is of great practical significance, for Shanghai is the largest city in China and serves as the national economic center and a global financial hub. By crawling the point of interest (POI) data from the Lianjia Real Estate and Gaode Map in the past decade and applying the multiscale geographically-weighted regression (MGWR) model, this study deeply explores the spatial characteristics of housing prices and their main influencing variables in Shanghai before and after the COVID-19 pandemic. Results show that housing prices in Shanghai kept rising even under the shock of the pandemic, especially in high-priced housing. After the pandemic, Shanghai’s housing price market polarization intensified. In addition, housing prices are very sensitive to location and have strong spatial heterogeneity. The influencing effects of different explanatory factors vary perceptibly in spatial heterogeneity as well as pre- and post- COVID-19.

Injury severity analysis of electric bike crashes in Changsha, Hunan Province: taking different lighting conditions into consideration

Abstract With the increasing use of electric bikes, electric bike crashes occur frequently. Analysing the influencing factors of electric bike crashes is an effective way to reduce mortality and improve road safety. In this paper, spatial analysis is performed by geographic information system (GIS) to present the hot spots of electric bike crashes during daylight and darkness in Changsha, Hunan Province, China. Based on the Ordered Probit (OP) model, we studied the risk factors that led to different severities of electric bike crashes. The results show that the main influencing variables include age, illegal behaviour, collision type and road factors. During daylight and darkness, elderly electric bike riders over the age of 65 years have a higher probability of fatal crashes. Not following traffic signals and reverse driving are significantly related to the severity of riders' injuries. In darkness, frontal collisions are significant factors causing rider injury. In daylight, more serious crashes will be caused in bend and slope road segments. In darkness, roads with no physically separated bicycle lanes increases the risk of riders being injured. Electric bike crashes are mainly concentrated in the commercial, public service and residential areas in the main urban area. In suburbs at darkness, electric bike riders are more likely to be seriously injured. Adding protection measures, such as improved lighting, non-motorized lane facilities and interventions targeting illegal behaviour in the hot spot areas can effectively reduce the number of electric bike crashes in complex traffic.

A three-stage decision-making process for cost-effective passive solutions in office buildings in the hot summer and cold winter zone in China

China, with the largest energy consumption system in the world, faces numerous challenges in achieving the government’s commitment to reach a carbon-peak and carbon–neutral target. As the most common public building type in terms of floor area, office buildings have great potential for energy saving and emissions reduction. To meet this target, building designers target passive solutions that can meet the thermal comfort needs of occupants and also reduce energy consumption. This study aims to develop a decision-making method to select optimal solutions from among tens of thousands of design options considering the factors of energy consumption, comfort, and cost. We developed a novel optimization decision approach with the above-mentioned three objectives. The model consists of three stages: 1) the establishment of the reference building model, 2) sensitivity analysis to identify the main influencing variables and 3) the establishment of the optimization and decision-making model by applying NSGA-II and TOPSIS methods. By applying this three-stage decision-making model, this paper first proposes cost-effective passive design solutions for office buildings throughout the Hot Summer and Cold Winter climate zone. Finally, an office building in Shanghai was chosen as a case study to demonstrate the practical implementation of the proposed solutions through a post-occupancy evaluation with a two-year energy auditing and thermal comfort survey. It is evident that the proposed solutions provide support for the new low energy building design guide for office buildings along with necessary revisions to the existing standards for the Hot Summer and Cold Winter climate zone in China.

Analysis of Factors Influencing Satisfaction with Public Services in Health Care: An Empirical Study Based on CGSS Data

Medical and health public services are closely related to citizens' lives and have always been widely concerned about people's livelihood. With the rapid development of domestic economy and society, the public's pursuit of health is also increasing, and the demand for medical and health public services has also taken diversified forms. The public's demand for the quality of medical and health public services provided by the government is also getting higher and higher. Therefore, how to improve public satisfaction with public health care services has become a popular topic of current research. This paper uses the method of empirical research to conduct a multiple linear regression analysis of the factors influencing satisfaction with health care public services based on CGSS data by using the main influencing variables including herd mentality, public perception of the government's responsibility to provide health care services, health state, medical insurance participation, family economic status, public social status, and age. On this basis, the actual influence effect is standardized and budgeted to provide directions and ideas for improving the satisfaction of health care public services.

Simulation and optimization of sweetening and dehydration processes in the pretreatment unit of a mini-scale natural gas liquefaction plant

This study focuses on the simulation of sweetening and dehydration processes for natural gas feed to achieve the liquefied natural gas specifications. The analysis and optimization of these two units have been carried out to reduce energy consumption. The sweetening and dehydration units lower the amount of carbon dioxide and water in treated gas to less than 50 ppmv and 0.1 ppmv, respectively. In the sweetening unit, due to the high energy consumption (45.63 kW) in the reboiler of the amine regeneration column, sensitivity analysis is used to investigate the variables causing high energy consumption. The optimum values for the main influencing variables were obtained by the response surface method. The optimization results in reboiler duty reduction by 30% compared to the base case simulation. In the dehydration unit, due to the high energy consumption of the furnace (100.7 kW) inlet stream temperature and regeneration temperature are analyzed as the influencing variables. By optimizing the unit, the energy consumption per mole production is reduced by 20% compared to the base case simulation. Through exergy analysis 836.13 kJ/kg CO2 exergy loss is estimated for sweetening plant and 5724.73 kJ/kg H2O exergy destruction for dehydration plant. The regenerator and absorber have a significant share of exergy losses in the sweetening plant and the furnace is the major contributor to exergy destruction in the dehydration plant.

ANALYSIS OF INCORPORATING OUTSOURCED TRANSPORT ACTIVITY BY THE FORESTRY COMPANY: AN ECONOMIC FEASIBILITY AND SENSITIVITY ANALYSIS

The analysis of economic feasibility in the forest transport segment is essential for companies to support decision-making. The objective of this work was to evaluate the economic return and to simulate a sensitivity scenario using the Monte Carlo technique when incorporating outsourced wood transport activity by the forestry company. The study was carried out in a company in the forestry segment located on the north coast of Bahia, Brazil, which uses a Biminhão (semi-truck with a flatbed trailer) as its base for transport. The costs, revenues and uncertainties involved in the activity were analyzed. The following viability criteria were used: NPV, EAV, IRR, APC, BCR and PayBack period. A sensitivity analysis was performed as a way to support the safety of the evaluated project. The economic evaluation was positive in all analyzed criteria (NPV US$ 129,231.54); VAE (US$18,827.22); (CMP US$ 2.62/m³); B/C (1.08), IRR (75%) and PayBack period (3.55 years). The probability of generating negative NPV results in the conducted simulation was 20.4%, with the main influencing variables being the freight value and the interest rate. Considering the data under study, incorporating wood transport activity with the use of a semi-truck with a flatbed trailer by the forestry company proved to be attractive and did not present a significant economic risk.

Optimization of tensile strength for heat treated micro-alloyed steel weldment

Welding and post weld heat treatment process variables play important roles in determining the quality of welds. Thus, to produce welded joints of desired microstructure with the requisite mechanical properties for optimal performance in service, the need for the analysis of the process variables requiring optimization to determine their influence on the response (output) is very vital. In this study, the optimal design approach was used to optimize the main influencing process variables of welding current, tempering temperature and holding time to determine their impact on the observable response (tensile strength). Through multiple regression analysis of the experimental data, the predicted response was expressed by the second-order polynomial equation in terms of coded values. The derived model was statistically analyzed to determine its significance and predictive capacity. The ANOVA results of the quadratic regression model used to fit the response showed that it is significant (P < 0.00001). The high coefficient of regression and adjusted regression coefficient values of 94.73% and 89.99% respectively, is an expression of the model degree of fitness. The model F-value of 19.99 greater than the P-value also attest to the model significant. Validation of the optimized predicted results revealed that they are in close consonant with the experimental results. This investigation has shown that the optimum values of welding current, tempering temperature and holding time for achieving optimal tensile strength of 258.141KN/m2 in the heat treated micro-alloyed steel weldment were 90.530 amp, 250.002 °C and 60.000 min respectively.

Study on habitat suitability and environmental variable thresholds of rare waterbirds

The conservation level of rare waterbirds reflects the quality of the regional ecological environment and wetlands, and suitable habitat patches and good environmental conditions are bases to support the activities of rare species in habitats. Establishing these conditions is also an important goal of habitat landscape and functional restoration. However, lack of these conditions limits population protection and habitat restoration of rare species. Based on the random forest (RF) algorithm and threshold indicator taxa analysis (TITAN), this paper performed habitat suitability assessment and environmental variable threshold analysis of rare waterbird species in Yancheng coastal wetlands. The results showed that the suitable area proportion of three waterbird species at different habitat sites was less than 20%. The unsuitable area proportions of red-crowned cranes and oriental storks at the CA habitat site were the highest, reaching 86.73% and 85.17%, respectively. In addition, analysis of the importance of environmental variables showed that the main influencing variables affecting the suitable habitat distribution of the three rare waterbirds were habitat type (T_hab), habitat area (A_hab), vegetation coverage (P_fvc), distance to farmland (D_far), distance to reeds (D_ree), ponds density (Ponds), distance to water surface (D_wat) and distance to main roads or seawalls (D_swa). These variables covered the type, area, coverage and distance indicators. With the exception of D_far, Ponds and D_swa, rare waterbirds had response thresholds to each environmental indicator, and these results supported the restoration of landscape structure and function of each habitat site. This study emphasized the importance of foods, water resources and hidden conditions for habitat selection in rare waterbirds. Finally, we proposed the maintenance and restoration patterns of the landscape structure and function of rare waterbird habitats, which are available for other coastal tidal wetlands.

Safety performance optimization of front bumper system based on pedestrian lower leg protection

Taking the front bumper system of a passenger car as the research object, a finite element model of pedestrian lower leg collision was first established, and the tibial acceleration at Y400 was found to be high in the performance analysis. Second, the sensitivity analysis was performed on the main parameters of the bumper system to determine the skin Thickness and foam density are the main influencing variables. Finally, a performance improvement scheme is proposed, and the lower leg tibia acceleration is reduced by 47.71% after the improvement, indicating that the scheme is conducive to lower leg protection.

Development of a Flexible Injection and Impregnation Chamber for Pultrusion of High Reactive Resins

Pultrusion is an established and efficient process for the production of continuous fiber-reinforced composites using resin systems such as unsaturated polyesters and vinyl esters. Highly reactive resins like polyurethane and amine epoxy have been in use for a few years. These resin classes stand out due to an extended spectrum of properties. This opens up new fields of application for the pultrusion technology, but poses challenges for the processing technology. Pot lives in the range of a few minutes require a continuous process: closed injection pultrusion (CIP). Various approaches to the design and layout of the internal geometry of the injection and impregnation chambers (ii-chamber) are the subject of ongoing research. Numerous factors influence the impregnation process in the ii-chamber as well as the quality of the resulting composite.Within the scope of this study, the influencing variables of the impregnation process in the ii-chamber have been analyzed and evaluated. The main influencing variables and their impact were determined. Based on the selection of the influencing variables, a concept for an ii-chamber has been developed using product development methods. This approach allows fast and flexible adjustment of the main influencing variables in the process. The chosen strategy enables the derivation of suitable die and process parameters for the processing of novel materials for pultrusion. The ii-chamber concept has been implemented and validated by experimental processing of a novel PU resin system in the pultrusion process.

Influence of chronotype on migraine characteristics.

The aim of this study was to investigate chronotype in migraine patients and possible influences on the clinical expression of the disease. During a one-year period, all consecutive patients admitted to two third-level headache centres with a new diagnosis of migraine were enrolled in a cross-sectional study. All subjects were submitted to the Morningness-Eveningness Questionnaire (MEQ-SA) and then classified in five different categories, from late to early-rising chronotype. Differences and trends among MEQ-SA categories and years from migraine onset, attacks' intensity and frequency were analysed first with analysis of variance, then with a multivariate/generalized linear model. One hundred seventy one migraine patients were included. Early-rising patients showed a lower migraine attacks frequency and longer disease duration with respect to late-rising patients. The categorical variable containing the five circadian types was able to identify a significantly different trend both for the monthly attacks frequency and for the disease duration (p < 0.0001 and p < 0.0001, respectively, analysis of variance). The results were also confirmed after correction for main influencing variables (multivariate/generalized linear model). The intensity of migraine attacks was not influenced by chronotype. According to the results of the present study, chronotype seems to influence number and duration of migraine attacks. Although sleep-wake cycle is a well-recognized factor able to influence thalamic-cortical synchronization, it usually does not receive appropriate consideration during migraine patients' assessment.