Multiple Linear Regression Models Research Articles

Characterizing indoor air quality and identifying factors influencing air quality at home microenvironment in Dhaka city

In low- and middle-income countries, indoor air pollution stands as a significant contributor to morbidity and mortality, prompting research into the factors influencing exposure at home in urban environments. Thus, this study was designed to investigate the indoor air quality and identifying factors influencing indoor air quality which help in targeting intervention to reduce indoor home air quality. The study conducted in Dhaka city involved 43 homes, where continuous monitoring of PM2.5 concentration was carried out over a 24-h period. Various factors related to home characteristics (i.e., home area and cooking fuel type), ventilation practices (i.e., duration of window opening), and indoor activities (i.e., cooking frequency, daily average cooking duration per meal, cleaning, smoking and use of mosquito coil and spray) were assessed to explore their impact on indoor air quality. Through the multiple linear regression model, the relationship between the factors and indoor pollutant concentrations was analyzed. The average PM2.5 concentration recorded in this investigation was five time higher in comparison to the World Health Organization's (WHO) 24-h guideline level for ambient air pollution. Four factors including outdoor air, home area, cooking duration and cleaning frequency were found to be significantly linked to indoor concentrations, collectively explaining 64 % of the variability in indoor PM2.5 levels. Outdoor air infiltration emerged as the most influential predictor of indoor levels, contributing significantly to indoor concentrations. The identified factors could assist in targeting interventions to reduce microenvironmental PM2.5 concentration at home.

Evaluation Method of Teaching Effect of Environmental Art and Design in Universities Based on Multi-Source Data Fusion Technology

The evaluation of teaching reform satisfaction is crucial for improving teaching quality. In the evaluation of satisfaction with teaching reforms in environmental art and design, issues such as the low reliability of evaluation indicator data can lead to unsatisfactory results. To address this, this paper proposes a new method for evaluating satisfaction with teaching reforms in environmental art and design based on multi-source data integration. By establishing principles for constructing a multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design, a four-level multi-source indicator system was determined. This system mainly includes one first-level indicator, four second-level satisfaction evaluation indicators, and several third- and fourth-level evaluation indicators. The construction of the multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design was completed. Using a four-part graph model and entropy weight calculation, the importance of the evaluation indicators for satisfaction with teaching reforms in environmental art and design was classified. The expert scoring method was introduced to correct the multi-source indicator data for evaluating satisfaction with teaching reforms in environmental art and design. Based on standardized indicator data, a penalty rule was added to the loss function of the multiple linear regression model to remove data noise, achieving pre-processing of the multi-source indicators for evaluating satisfaction with teaching reforms in environmental art and design. After completing the above process, first collect multi-source data from questionnaire surveys, student feedback, etc., and preprocess and map it to a unified ontology class. Subsequently, data features are extracted through non-negative matrix factorization, and the similarity between object types is calculated. Finally, based on the importance, redundancy, and interaction of data sources, an integrated quality model is designed to effectively integrate multiple sources of data, construct a satisfaction evaluation model, and output the score of satisfaction evaluation. The experimental results show that the proposed method has the highest satisfaction index, reaching about 0.99, indicating a high degree of recognition for the reform; when the amount of integrated data changes, the proposed method maintains the best integratison reliability. Even with an increase in data volume, its reliability only slightly decreases and stabilizes at a high level; meanwhile, the average absolute error of the proposed method is the lowest, consistently below 2.5%, indicating a high level of stability and accuracy in its evaluation results. The above results confirm that the proposed method can effectively evaluate the satisfaction of environmental art design teaching reform and improve teaching quality.

Does corporate governance differ by sector? An analysis under good practice criteria. The case of Spain.

The aim of this paper is to analyse how the factors remuneration, supervision and board structure influence the good corporate governance of companies in the Spanish Continuous Market. This paper develops, for the first time, an index based on the recommendations defined in the Good Governance Code of Listed Companies. This paper measures remuneration, monitoring and governance structure and employs a multiple linear regression model modelling corporate governance as a latent factor. Based on this model, this research presents empirical evidence of the relationship between corporate governance and the defined variables, considering Spanish Continuous Market firms disaggregated by sector. To date, there are no studies that have taken into account the analysis for all sectors in the same country. Among the conclusions, the research finds that the larger a company is, the better the mechanisms for providing an optimal degree of governance, as is the case in the oil, energy and technology sectors. In another sense, the higher the number of proprietary directors the lower the levels of good governance, mainly in basic materials, industrial, construction and consumer goods and services companies. The empirical results also justify the inclusion of Corporate Governance-related factors in governance regulation.

Analysis and prediction of energy consumption in office buildings with variable refrigerant flow systems: A case study

Accurately predicting building air-conditioning energy consumption is particularly important for energy management. However, for small datasets, there is a lack of simple and effective prediction methods applicable to multiple buildings. In order to fully explore the energy consumption mode of Variable Refrigerant Flow (VRF) air conditioning system and improve the accuracy of the model. This study uses the weather data of an outdoor meteorological data collection system and the air conditioning system and energy consumption data of an office building in Jinan. Initial analysis revealed that occupant behavior and daily variations in outdoor air temperatures significantly impact the energy consumption of air conditioning systems. Then, by applying the random forest algorithm and Pearson correlation analysis, the study identified daily average outdoor air temperature and VRF indoor unit running time as critical features for prediction. Subsequently, three machine learning models—Multiple Linear Regression (MLR), Back Propagation Neural Network (BPNN), and Long Short-Term Memory (LSTM) neural network—were developed and assessed using a 4:1 training-to-testing set ratio. Comparative analysis revealed that all models performed robustly, with the MLR model exhibited superior predictive accuracy (R2 (Coefficient of Determination) = 0.98, MAE (Mean Absolute Error) = 0.005 kWh/(m2·day), and RMSE (Root Mean Square Error) = 0.007 kWh/(m2·day) for the testing set) and simplicity. Furthermore, the effectiveness of the MLR model in handling small sample sizes was confirmed through cross-validation of data from two additional office buildings, highlighting its suitability for predicting energy consumption of VRF air conditioning systems in office buildings.

Research on Factors Influencing Medical Insurance Cost in the US

After the COVID-19 epidemic, the growing concern over medical insurance cost and the factors that influence it are increasingly coming to the forefront. These factors include age, sex, body mass index, number of children, smoker or non-smoker and region. Therefore, this paper analyzes the impact of six factors on medical insurance costs by utilizing data on medical insurance expenses of 2,772 individuals in the US. The analysis employs a multiple linear regression model and the stepwise regression method. It is concluded that age, body mass index, number of children, smoker or non-smoker and region have a significant impact on medical insurance cost with the largest regression coefficients for age, body mass index and smoker or non-smoker. This shows that it is physiological factors that have the greatest impact and therefore it is recommended that people should better maintain good health in order to reduce medical insurance cost and save money.

The Geographical Conditioning of Regional Differentiation Characterising the COVID-19 Pandemic in European Countries.

The aim of this paper is to assess the influence of selected geographical factors on the diversity of the development of the COVID-19 pandemic in Europe's regions, and on its dynamics across the continent. The work took into account 250 of NUTS-2 regions. The datasets included the course of the COVID-19 pandemic (two dependent variables), intervening actions (four variables of the research background), and potential environmental and socio-economic conditioning (twelve independent variables). The dependent variables' set was composed of two indexes: morbidity and temporal inertia. The temporal scope of the research was 23 March 2020-15 May 2022, with weekly resolution. By means of multiple linear regression model, the influence of the administrative actions and of the selected natural and socio-economic factors was assessed. Finally, a synthetic Regional Epidemic Vulnerability Index (REVI) for each individual region was calculated. It allowed us to classify the regions into three categories: resistant, neutral, or sensitive. REVI's spatial distribution indicates that the zone of above-average vulnerability occurred in the western part of Europe and around the Alps. Therefore, focus ought to extend beyond regional statistics, towards spatial relationships, like contiguous or transit position. This research also validated the strong impact of national borders.

Soil organic carbon and total nitrogen stocks related to land use and basic environmental properties − assessment of soil carbon sequestration potential in different ecosystems

Soil organic carbon (SOC) and total nitrogen (TN) stocks are crucial for the development of wild plants and crops. This paper examines the current SOC and TN stocks and their variability within land uses under different environments, assessing the relationships between SOC and TN stocks with basic environmental properties and quantifying the magnitude of SOC sequestration potential for a better land use management. We studied 991 soil profiles, from steppe to wet mountain-soils. The land use was essential in influencing soil organic C and total N stocking, with the forestland showing the significantly highest SOC stocks specifically in mountain soils, followed by grassland and cropland. Altitude, clay content, pH and plant available phosphorous and potassium were other influencers of SOC and TN stocks. The best predictive multiple linear regression model explained 68 % of the 0.5 m depth SOC stock variability for forest, 61 % for grassland and 37 % for cropland, while Random Forest model explained 70 %, 65 %, and 28 % for the same land uses. The obtained models rank factors contribution and may be useful in management. Lands having the highest C sequestration potential occurred within fine-textured soils, mainly in croplands. The most favorable soil depth for further C sequestration is below C-saturated topsoil and this could be achieved by deep-rooting crops and conservative technologies. Additionally, changing some low-fertile soils of cropland into forestland or grassland would improve SOC sequestration. These measures might contribute to sequester additional C amounts in soils, in order to bolster initiatives for climate-change mitigation and adaptation.

Investigation Upon Factors Contributing to Housing Price Variations-Specifically Sydney for Reference

The purpose of the article is to determine the small aspects that can affect the overall housing prices around the globe. It will be established by looking into one specific area and finding the relationship between them. This research will be using a multiple linear regression model to find the connection. The study used 300 samples and 9 variables each, all are possible variables that can influence overall house price. The data are from Sydney Urban areas collected during the 2020-2021 house market. For the outcome to be valuable, this research will be comparing the significance and VIF value for those variables to find out the biggest connection. Through this process, the null data was deleted as much as possible. As a result, this article discovered that Safety and Median house rent are the biggest influencing factors to the house price. The two factors are the most considered during a house purchase. And the price of Sydney urban houses can be determined by those two factors.

Oral Disease and Atherosclerosis May Be Associated with Overlapping Metabolic Pathways.

Dental caries and periodontitis are among the most prevalent chronic diseases worldwide and have been associated with atherosclerotic cardiovascular diseases (ASCVD). This study aimed to determine (1) the independent associations between subclinical ASCVD markers (carotid intima media thickness [CIMT] and coronary artery calcification [CAC]) and quantitative indices of oral disease including the decayed, missing, and filled teeth (DMFT) index, gingivitis parameters, periodontal status, and number of teeth lost and (2) the extent to which metabolites altered in individuals with oral disease overlapped with those altered in individuals with ASCVD. We used data from 552 participants recruited through the Dental Strategies Concentrating on Risk Evaluation project. Oral examinations were conducted, and CIMT and CAC were measured. Multiple linear regression models were constructed with CIMT and CAC as dependent variables in the epidemiologic analysis. In the metabolomic analysis, logistic or linear regression was used to test 1,228 metabolites for association with each phenotype adjusted for age, sex, race, blood pressure, smoking, diabetes, cholesterol, high-sensitivity C-reactive protein, and interleukin-6. None of the oral disease markers were significant predictors of ASCVD markers in the fully adjusted models. However, critical lipid and lipid-signaling pathway metabolites were significantly associated with gingivitis, periodontitis, and DMFT: the lysophospholipid pathway (odds ratio [OR] = 2.29, false discovery rate [FDR]-adjusted P = 0.038) and arachidonate with gingivitis (OR = 2.35, FDR-adjusted P = 0.015), the sphingolipid metabolism pathway with periodontitis (OR = 2.09, FDR-adjusted P = 0.029), and borderline associations between plasmalogen and lysophospholipid pathways and DMFT (P = 0.055). Further, the same metabolite from the sphingolipid metabolism pathway, sphingomyelin (d17:1/14:0, d16:1/15:0), was inversely associated with both CIMT (β = -0.14, FDR-adjusted P = 0.014) and gingivitis (OR = 0.04, FDR-adjusted P = 0.033). The discovery of a common sphingomyelin metabolite in both disease processes is a novel finding suggesting that gingivitis and periodontitis may be associated with some overlapping metabolic pathways associated with ASCVD and indicating potential shared mechanisms among these diseases. The same metabolites may be altered in atherosclerosis and oral disease. Specifically, a common sphingomyelin metabolite was inversely associated with gingivitis and carotid intima media thickness, a subclinical marker of atherosclerotic cardiovascular disease. These findings can provide valuable insights for future mechanistic studies to establish potential causal relationships, with the hope of influencing disease prevention and targeted early treatment.

Association of metabolically healthy obesity with risk of heart failure and left ventricular dysfunction among older adults.

Obesity is major cause of heart failure (HF), but it is related with a better prognosis among the elderly. Therefore, we aimed to examine whether metabolically healthy obesity (MHO) in late life increases HF risk and is reflected in impaired left ventricular (LV) function. The participants were grouped into four metabolic phenotypes based on obesity and metabolic status: metabolically healthy non-obesity (MHN), MHO, metabolically unhealthy non-obesity (MUN), metabolically unhealthy obesity (MUO). Association of metabolic phenotypes with LV function was evaluated using multiple linear regression models. And association between metabolic phenotypes and risk of HF was assessed using multivariable logistic regression models. In addition, we validated the association of metabolic phenotypes and HF risk in a separate longitudinal cohort. In the primary cohort of 6335 participant, there were 434 participants diagnosed with HF. Compared to MHN participants, the risk of HF was higher among older individuals with MUN (OR = 1.51 [95% CI: 1.14-1.99]) and MUO (OR = 2.01 [95% CI: 1.39-2.91]), but not older individuals with MHO (OR = 0.86 [95% CI: 0.30-2.43). Regarding to LV function, worse LV diastolic function was noted among MUN and MUO individuals rather than MHO individuals. Older adults with MHO were also not associated with risk of HF in the validation cohort. Among older individuals, the metabolic health status might modify the association of obesity with risk of HF and LV diastolic dysfunction. Worse LV diastolic function and higher risk of HF were just noted in individuals with MUO, but not in those with MHO.

Experimental Study on Proportion Optimization of Rock-like Materials Based on Genetic Algorithm Inversion.

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water-paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σc, tensile strength σt, shear strength τf, elastic modulus E, Poisson's ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water-paste ratio had extremely significant effects on σc, σt, τf, E, ν and c. The iron powder content had a highly significant effect on σc, σt, τf and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σc, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water-paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials.

Estimation of nighttime PM2.5 concentrations over Seoul using Suomi NPP/VIIRS Day/Night Band

With rapid economic development and urban growth, Seoul experiences severe air pollution due to fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), which is detrimental to human health. Although recent studies have extensively focused on estimating daytime PM2.5 concentrations using various types of satellite data, there remains a significant lack of research on nighttime PM2.5 estimations. This study estimated nighttime PM2.5 in Seoul from December 2018 to November 2019 using multiple linear regression (MLR) and random forest (RF) models. These models, which incorporated data on radiance, moon illumination fraction, and terrain height from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi National Polar-orbiting Partnership satellite covering all moon phases, also utilized meteorological data from the ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF). To address multicollinearity, seasonal models were developed using forward stepwise regression and variance inflation factor analysis. DNB radiance analysis indicates that the high intensity of artificial light sources in Seoul significantly reduces the impact of moonlight, leading to notable changes in the DNB radiation associated with PM2.5 concentrations. Consequently, this study estimated nighttime PM2.5 over Seoul across all moon phases. These estimates were then validated through 10-fold cross-validation. The RF model exhibited superior accuracy, with a coefficient of determination (R2) of 0.65–0.90, compared to MLR, with R2 of 0.15–0.50, reflecting seasonal fluctuations in the model performance. The developed models can be applied to estimate reliable nighttime PM2.5 concentrations in megacities with strong artificial light sources, utilizing a comprehensive dataset from satellite observations for all moon phases. Additionally, our findings can serve as scientific data for establishing environmental policies by providing valuable insights into understanding air pollution primarily caused by PM2.5.

Sex/gender effects of glial reactivity on preclinical Alzheimer's disease pathology.

Glial reactivity may contribute to sex/gender differences in Alzheimer's disease (AD) pathophysiology. Here, we investigated the differential effect of cerebrospinal fluid (CSF) glial markers on AD pathology and neurodegeneration by sex/gender among cognitively unimpaired older adults at increased risk of developing AD. We included 397 participants from the ALFA+ cohort with CSF Aβ42/40, p-tau181, sTREM2, YKL40, and GFAP, magnetic resonance imaging-based hippocampal volume (n = 299), and amyloid burden (centiloids) measured with [18F] flutemetamol positron emission tomography (n = 341). We ran multiple linear regression models to assess the association between glial markers, AD pathology and hippocampal volumes and their interaction with sex/gender, usingFalse Discovery Rateto correctfor multiple comparisons. Glial markers significantly contributed to explain amyloid burden, tau pathology, and hippocampal volumes, beyond age and/or primary AD pathology in a sex/gender-specific manner. Compared to men, women showed increased amyloid burden (centiloids) and CSF p-tau181 with increasing levels of sTREM2 and YKL40, and YKL40 and GFAP, respectively. Compared to women, men with greater tau burden showed lower hippocampal volumes as CSF YKL40 levels increased. Overall, our findings suggest that glial reactivity may contribute to sex/gender differences in AD progression, mostly, downstream amyloid. Further research identifying sex/gender-specific temporal dynamics in AD development is warranted to inform clinical trials.

Moderating effects of policy measures on intention to adopt autonomous vehicles: Evidence from China

Autonomous vehicles (AVs) can potentially revolutionize the transportation system, but the extent of their impact may depend on users’ attitude and AV-related policies. This paper seeks to provide a holistic view of the impacts of policy, attitudinal, and sociodemographic factors on AV adoption intention. An extension to the original Technology Acceptance Model is proposed by incorporating perceived enjoyment (i.e., how enjoyable respondents think using an AV will be) and policy factors. Four policy factors include the availability of financial incentives, awareness campaigns, traffic policies, and legislative measures. Using 1,831 survey responses in China, multiple linear regression models were estimated to quantify the direct impacts of the proposed policy and attitudinal factors on AV adoption intention. They also illustrate the moderating effects of these policies on the relationships between attitudinal factors and AV adoption intention. The study findings may be used to design future policy measures to facilitate a smooth transition to an era of AVs.

Forget About Memory: Disentangling the Amnestic Syndrome in Idiopathic Normal Pressure Hydrocephalus.

Idiopathic normal pressure hydrocephalus (iNPH) can present with both episodic amnestic syndrome and biomarkers of Alzheimer's disease (AD) pathology. To examine the associations between amnestic syndrome and cerebrospinal fluid (CSF) AD biomarkers in iNPH and the CSF tap test response in iNPH patients with amnestic syndrome. We used the Free and Cued Selective Reminding Test to divide iNPH into amnestic and non-amnestic patients. We compared their clinical, biological, and radiological characteristics and examined the reversibility of gait spatiotemporal parameters and neuropsychological performances after a CSF tap test. Univariate and multiple linear regression models examined the association between memory performance and clinical-biological characteristics. Sixty-two non-amnestic patients (mean age 77.0±7.0 years, 38.7% female) and thirty-eight amnestic patients (mean age 77.0±5.9 years, 36.8% female) presented similar levels of AD biomarkers and clinical-radiological profiles. Global cognition and education levels were lower in the amnestic iNPH group. We found no association between AD biomarkers and memory performances (total tau: β= -4.50; 95% CI [-11.96;2.96]; p = 0.236; amyloid-β (1-42): β= 8.60, 95% CI [-6.30;23.50]; p = 0.240). At baseline, amnestic iNPH patients performed worse on executive functions, attention, and gait speed but improved similarly to the non-amnestic iNPH patients after the tap test. In our clinical sample of iNPH patients, we confirm the lack of specificity of the amnestic profile for predicting AD pathology. Clinicians should not preclude amnestic iNPH patients from undergoing an invasive procedure of CSF derivation.

Exploring the inclusion of soil management practices in erosion models towards the improvement of post-fire predictions

Wildfires are recognized for having a strong impact on forest soils, a situation aggravated by inadequate pre-fire land management practices. Land management operations, such as plowing, are routinely carried out for cultural reasons and can impact soils for decades after their implementation. Therefore, it is crucial to take into account the pre-fire land management history when predicting post-fire sediment losses in burnt areas. This consideration is critical for a realistic assessment of soil erosion risk and, consequently, for effectively implementing emergency stabilization and/or rehabilitation measures.The aim of the study was to integrate pre-fire land management practices into erosion models, to enhance post-fire sediment losses predictions at slope scale. To accomplish this goal, both Multiple Linear Regression (MLR) and the revised-Morgan-Morgan-Finney model (revised-MMF) were applied in the Colmeal burnt area (Central Portugal). These models were adapted to account the impacts of different management options, specifically no plowing, downslope-plowing and contour-plowing, on the erosive response following a wildfire.The results revealed fluctuations in the performance of both models across different soil management, and over time since the wildfire. Despite the observed variability, it is important to highlight the positive outcomes achieved with the revised-MMF model over the three monitoring years where contour-plowing was applied. These results demonstrate that the best model performances are achieved when soil management is individualized and analyzed independently. Similarly, the MLR model exhibited improved performance when incorporating management practices into its predictions. This study confirms that disturbances on topsoil, whether caused by wildfires or soil management operations, play key roles in driving change in soil erosion. Hence, integrating these factors into models is essential for providing relevant information for the development of mitigation and/or restoration strategies in areas at high risk of erosion.

Foreign Investment and Governance Shaping SEZ Performance in Uzbekistan

This study examines the impact of Foreign Direct Investment (FDI) on the performance of Special Economic Zones (SEZs) in Uzbekistan, with a focus on export growth, job creation, and infrastructure development. While SEZs are globally recognized for their contribution to economic growth, limited research exists on their outcomes in Uzbekistan. To address this gap, a multiple linear regression model was applied to data from 100 stakeholders, including policymakers, investors, and businesses within Uzbekistan's SEZs. The analysis revealed that FDI has a significant positive impact on SEZ performance, with a coefficient of 0.45, followed by governance quality (0.35) and infrastructure (0.25). The findings underscore the importance of foreign investment in fostering industrial growth and economic diversification. The study suggests that improving governance structures and infrastructure within SEZs is essential to fully leverage the benefits of FDI and recommends policy measures such as better investment incentives and streamlined bureaucratic processes to attract more foreign investors.

Quality of life after hospitalization for COVID-19

Objective: To analyze quality of life (QoL) and associated factors in individuals after hospitalization due to COVID-19. Method: Cross-sectional study of patients hospitalized for COVID-19. The WHOQOL-BREF instrument was used for QOL analysis. A multiple linear regression model was used to evaluate the QOL of the stakeholders. Results: The highest mean QOL was observed in the social relationships domain (14.97 ± 3.43), followed by the psychological (14.53 ± 2.07), environmental (14.20 ± 2.49) and physical domains (12.75 ± 2.10). For each additional year of age, there was an increase of 0.44 points in the physical domain, 0.06 in the social relationship domain and 0.49 in the environmental domain. Individuals earning up to 2 times the minimum wage had a reduction of 1.60 points in the environmental domain; retired or a pensioner had a reduction of 1.84 points in the physical domain score, 1.40 in the psychological domain score and 2.62 in the social domain score in the ICU had a negative impact of 1.52 points on the scores in the social relationship domain. Conclusions: After hospitalization for COVID-19, individuals presented a good perception of their QOL. Demographic, social and clinical aspects influenced the perception of QOL, which indicates the need for the adoption and implementation of public policies aimed at minimizing these differences.

The Influence of Social Competition and Maths Anxiety on Game Performance

Digital Game-Based Learning (DGBL) uses serious games to deliver educational content. DGBL has the potential to aid children’s development of maths skills, providing educators with alternatives to traditional mathematics teaching. However, the efficacy of serious games can be affected by their attributes, including whether they are single or multi-player. While competition in a multi-player environment may enhance players’ interest and motivation, potential social pressure may lead to anxiety and affect performance. In maths education, there are additional challenges. Mathematics Anxiety (MA), the negative emotional response to mathematics, may interact with game features and impact players’ experience. This study assesses players' performance in a digital maths game called ’Seven Spells’ across two different game modes (vs. Human and vs. CPU), and it also investigates whether MA levels impacted players’ performance. 43 children from two classes in an Irish primary school participated in thisstudy. Each class played a different game mode of the ‘Seven Spells’ game, one group playing against computer-controlled characters (vs. CPU) and the other playing against their classmates (vs. Human). The game mechanics were identical between the two competition game modes. The children completed the modified Abbreviated Mathematics Anxiety Scale (mAMAS) and a mathematics questionnaire before participating in the intervention, which included 4 sessions. They also participated in a focus group session at the end of the intervention. Data on their gameplay were collected throughout the first three sessions and analysed through multiple linear regression models to investigate possible connections between MA, maths knowledge, game modes and game performance. The results of the multiple linear regressions indicate that game performance was positively influenced by the duration of gameplay and the complexity of moves — measured by the number of moves and cards used — while being negatively affected by MA. However, a significant interaction between MA and the competition game mode was observed, suggesting that MA had a more pronounced negative impact on players in the vs. Human game mode compared to the vs. CPU game mode.

Assessment of climatic and anthropogenic influences on vegetation dynamics in China: a consideration of climate time-lag and cumulative effects.

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration.