Stepwise Multiple Regression Model Research Articles

The effects of changes in thrombocyte indices, on-on-pump time on prognosis in open heart surgery.

Extracorporeal perfusion systems utilized in open-heart surgeries with cardiopulmonary bypass can affect multiple body systems. The primary adverse effects of Extracorporeal Perfusion Systems (EPS) on the hematological system include postoperative bleeding and coagulation issues. The aim of this study is to evaluate the effects of on-pump time and cross-clamp duration on total platelet count (PLT) and platelet indices in open-heart surgeries performed using cardiopulmonary bypass. This evaluation is achieved by comparing PLT, mean platelet volume (MPV), platelet distribution width (PDW), and plateletcrit (PCT) before and after surgery. For this study, the laboratory results of a total of 392 patients who underwent open-heart surgery with cardiopulmonary bypass at the Department of Cardiovascular Surgery, Istanbul University Cerrahpaşa Institute of Cardiology, between 2017 and 2018 were retrospectively analyzed. The stepwise multiple linear regression model, in which postoperative PLT was the dependent variable and other parameters were independent variables, revealed that the main determinants of the significant change in postoperative PLT were the duration of device use, along with preoperative PLT and PDW. Our results indicate that on-pump time, along with preoperative PLT and PDW values, is the primary determinants of changes in PLT and PCT in open-heart surgeries with cardiopulmonary bypass. Considering the patient's preoperative platelet count and PDW values in such surgeries may be useful in preventing postoperative adverse events.

Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm

Achieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it is important for optimizing field management measures and improving crop productivity. To accurately and quickly predict citrus yield, this study obtained multispectral images of citrus fruit maturity through an unmanned aerial vehicle (UAV) and extracted multispectral vegetation indices (VIs) and texture features (T) from the images as feature variables. Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), and multiple stepwise regression (MSR) models were used to construct citrus fruit number and quality prediction models. The results show that, for fruit number prediction, the XGB model performed best under the combined input of VIs and T, with an R2 = 0.792 and an RMSE = 462 fruits. However, for fruit quality prediction, the RF model performed best when only the VIs were used, with an R2 = 0.787 and an RMSE = 20.0 kg. Although the model accuracy was acceptable, the number of input feature variables used was large. To further improve the model prediction performance, we explored a method that utilizes a hybrid coding particle swarm optimization algorithm (CPSO) coupled with XGB and SVM models. The coupled models had a significant improvement in predicting the number and quality of citrus fruits, especially the model of CPSO coupled with XGB (CPSO-XGB). The CPSO-XGB model had fewer input features and higher accuracy, with an R2 > 0.85. Finally, the Shapley additive explanations (SHAP) method was used to reveal the importance of the normalized difference chlorophyll index (NDCI) and the red band mean feature (MEA_R) when constructing the prediction model. The results of this study provide an application reference and a theoretical basis for the research on UAV remote sensing in relation to citrus yield.

Estimating soil salinity level by pedotransfer functions

Abstract In this study, the electrical conductivity (EC) of cultivated fields were predicted by pedotransfer functions (PTFs) using basic soil properties as variables in stepwise analyses. Before using PTFs, 207 soil samples were divided into development and validation datasets. This division was done 10 times to assess the accuracy and reliability of the PTFs. According to the principal component analysis (PCA), the EC values had higher load in PC1 and PC2 with clay, exchangeable Na, Ca, Mg and K contents. The EC values had significant positive correlations with pH, clay, exchangeable Ca, Mg, K, Na content, and negative correlations with silt and sand content. According to the stepwise analyses, 10 PTFs or linear multiple regression models were obtained using development data sets. The accuracy in development data sets and reliability in the validation data sets for these PTF models were assessed with R2 and RMSE values. The higher mean R2 and the lower mean RMSE values were obtained in the development data set when compared to the validation data set. The PTF-7 including clay, exchangeable Na, Mg and Ca was suggested to predict soil EC values of cultivated fields. It was determined that clay and exchangeable Na, Mg and Ca content were the most effective soil properties on predicting the EC values of cultivated fields. Keywords: Salt, EC, exchangeable cations, soil texture

Relationship between personal meaning organisations and psychological and personal well-being in a Chilean sample.

The aim of this study was to evaluate the relationship between personal meaning organisations (PMO) and aspects of psychological and personal well-being in an adult population. The sample consisted of 104 adults (36.52±14.85 years, 56 women). Sociodemographic variables, well-being and PMO were evaluated. The following instruments were used: Mini Questionnaire of Personal Organization (MQPO), Personal Wellbeing Index (PWI) and Psychological Well-Being Scale (PWB). The statistical analysis included summary measures, internal consistency (Cronbach’s alpha), Pearson’s correlation coefficient, and stepwise multiple linear regression models; a significance level of p<.05 was adopted. Significant negative correlations between PMO scores and levels of psychological and personal well-being were found. In the regression models, strongest predictor was Detached PMO, followed by Contextualised PMO, which had a negative influence on well-being. Detached PMO had a negative impact on levels of personal well-being and especially on psychological well-being.

Relationship Between Geriatric Nurses' Spiritual Care Perceptions and Professional Attitudes, a Cross-Sectional Study.

This study, based on the self-determination theory, aims to explore the current situation of geriatric nurses' perceptions of spiritual care and its relationship with their professional attitude. It was conducted from February to May 2022. As the population ages, it is necessary to actively implement coping strategies. Geriatric nurses are important human resources, so we should pay attention to their perception of spiritual care for the elderly and their attitude towards the nursing profession. A cross-sectional survey was conducted. A total of 488 geriatric nurses engaged in elderly care were recruited from different hospitals in China. An online questionnaire was used to assess geriatric nurses' sociodemographics, their perception of spiritual care and their professional attitude. This study's findings indicate that the overall average score of geriatric nurses' perception of spiritual care was at a medium level (173.64 ± 27.87). Geriatric nurses' professional attitude was positively correlated with spiritual care perception (p < 0.001). The multiple stepwise linear regression model (n = 488) had an explained variance (R2 = 0.216), and three factors such as the need to enhance the ability of spiritual care through lectures and other methods were the main predictors of the perception level of spiritual care among geriatric nurses (p < 0.001). The findings demonstrated that the enhancement of professional attitudes among geriatric nurses can effectively augment their perception and implementation of spiritual care. This study's result is helpful for nursing managers to identify the weak links in the overall nursing services for elderly patients, thereby carrying out relevant intervention measures, improving the perception level of spiritual care of geriatric nurses, optimising the quality of elderly care services and achieving healthy aging. No patient or public contribution. The study focused on the current situation of geriatric nurses' perceptions of spiritual care and its relationship with their professional attitude. Therefore, the involvement of patients or the public was not deemed necessary for achieving the specific research objectives. The reporting of the study was guided by the STROBE checklist.

AI-enhanced video drama-making for improving writing and speaking skills of students learning English as a foreign language

ABSTRACT Advanced technologies, such as artificial intelligence (AI), have been used by teachers to enhance English as a Foreign Language (EFL) learning. However, few studies have employed AI in the making of video dramas to facilitate EFL learning. In this study, AI was used to recognize the authentic context of a drama and provide students with meaningful lexical resources, including vocabulary and sentence structures, to inspire them in their writing. Video-to-text recognition (VTR) and AI methods were used to develop an AI-enhanced video drama (AI-EVD) application for EFL learning, and its effectiveness for improving EFL writing and speaking was investigated by recruiting 77 university students in three groups: an experimental group (EG), which used the app, and control groups, which used the app without AI features (CG1) or participated in conventional learning (CG2). Learning achievement in the EG was significantly higher than that in CG1 or CG2. An analysis of the correlation between learning behavior and learning achievement showed that VTR-generated vocabulary, generative pretrained transformer (GPT)-generated sentences, and a pronunciation correction mechanism significantly improved the students’ performance. Further analysis with a stepwise multiple regression model revealed that the GPT-generated sentences feature was the learning behavior with the strongest effect on writing and speaking skills. Moreover, the AI-EVD application was perceived as being easy to use, helpful, and engaging according to the results of student questionnaires and interviews. Therefore, the AI-EVD application can motivate EFL students, enable them to produce meaningful video dramas, and improve their writing and speaking skills.

Hyperspectral Remote Sensing Combined with Ground Vegetation Surveys for the Study of the Age of Rodent Mounds

Background: Rodents severely damage the ecological environment of grasslands, and rodent mounds of different ages require distinct management strategies. Understanding the age of these mounds aids in formulating targeted restoration measures, which can enhance grassland productivity and biodiversity. Current surveys of rodent mounds rely on ground exposure and mound height to determine their age, which is time-consuming and labor-intensive. Remote sensing methods can quickly and easily identify the distribution of rodent mounds. Existing remote sensing images use ground exposure and mound height for identification but do not distinguish between mounds of different ages, such as one-year-old and two-year-old mounds. According to the existing literature, rodent mounds of different ages exhibit significant differences in vegetation structure, soil background, and plant diversity. Utilizing a combination of vegetation indices and hyperspectral data to determine the age of rodent mounds aims to provide a better method for extracting rodent hazard information. This experiment investigates and analyzes the age, distribution, and vegetation characteristics of rodent mounds, including total coverage, height, biomass, and diversity indices such as Patrick, Shannon–Wiener, and Pielou. Spectral data of rodent mounds of different ages were collected using an Analytical Spectral Devices field spectrometer. Correlation analysis was conducted between vegetation characteristics and spectral vegetation indices to select key indices, including NDVI670, NDVI705, EVI, TCARI, Ant, and SR. Multiple stepwise regression and Random Forest (RF) inversion models were established using vegetation indices, and the most suitable model was selected through comparison. Random Forest modeling was conducted to classify plateau zokor rat mounds of different ages, using both vegetation characteristic indicators and vegetation indices for comparison. The rodent mound classification models established using vegetation characteristic indicators and vegetation indices through Random Forest could distinguish rodent mounds of different ages, with out-of-bag error rates of 36.96% and 21.74%, respectively. The model using vegetation indices performed better. Conclusions: (1) Rodent mounds play a crucial ecological role in alpine meadow ecosystems by enhancing plant diversity, biomass, and the stability and vitality of the ecosystem. (2) The vegetation indices SR and TCARI are the most influential in classifying rodent mounds. (3) Incorporating vegetation indices into Random Forest modeling facilitates a precise and robust remote sensing interpretation of rodent mound ages, which is instrumental for devising targeted restoration strategies.

An in-depth multiscale analysis of farmland abandonment and recultivation dynamics in the Yangtze River Delta, China: A landscape ecology perspective empowered by google earth engine

Farmland abandonment represents the extreme utilization of marginal land and results from the interplay between humans and the environment. In the Yangtze River Delta region of China, a significant economic hub, understanding the landscape patterns and driving mechanisms of farmland abandonment and recultivation land is crucial for regional sustainable development. This study aims to map the extent of farmland abandonment in 2020 and recultivation land in 2021 across the Yangtze River Delta, analyze the landscape patterns of farmland abandonment and recultivation land on multiple scales, and explore their driving mechanisms. Utilizing multi-source data and the Land Use Trajectory Change Method on the Google Earth Engine platform, we classified land use and identified farmland abandonment, fallow land, active farmland, and recultivation land. Spatial statistical analysis was conducted to investigate the distribution and patterns of farmland abandonment and recultivation land at Yangtze River Delta, provincial, and municipal scales. Additionally, a stepwise multiple linear regression model was employed to dissect the main driving forces of farmland abandonment and recultivation land. Results revealed that farmland abandonment in the Yangtze River Delta region exceeded an abandonment rate of 10%, with a reclamation rate in the following year of less than 30%. The spatial distribution of farmland abandonment and recultivation land varied significantly across different scales. On the Yangtze River Delta scale, farmland abandonment was positively influenced by altitude, distance to rivers, and temperature, while recultivation land was positively correlated with night light intensity. At the provincial and municipal scales, the driving forces exhibited scaling effects, with different factors becoming significant. Our findings contribute to a deeper understanding of the complexity and diversity of farmland abandonment in the Yangtze River Delta region. The results provide a scientific basis for formulating land use policies, promoting sustainable agricultural development, and ensuring ecological balance. By uncovering the land use status and ecosystem balance influenced by human-environment interactions, this study offers insights into achieving a harmonious coexistence between economy, society, and ecology in the Yangtze River Delta region.

Cerebello-Cerebral Resting-State Functional Connectivity in Poststroke Aphasia.

Introduction: The influence of the cerebellum in poststroke aphasia recovery is poorly understood. Despite the right cerebellum being identified as a critical region involved in both language and cognitive functions, little is known about functional connections between the cerebellum and bilateral cortical hemispheres following stroke. This study investigated the relationship between chronic poststroke naming deficits and cerebello-cerebral resting-state functional connectivity (FC). Methods: Twenty-five cognitively normal participants and 42 participants with chronic poststroke aphasia underwent resting-state functional magnetic resonance imaging. Participants with aphasia also underwent language assessment. We conducted regions of interest (ROI)-to-ROI analyses to investigate the FC between the right cerebellar Crus I/II (seed ROI; Cereb1r/Cereb2r) and bilateral cortical language regions and compared these results to cognitively normal controls. Single-subject connectivity parameters were extracted and used as independent variables in a stepwise multiple linear regression model associating Boston Naming Test (BNT) score with FC measures. Results: FC analyses demonstrated correlations between the right cerebellar Crus I/II and both left and right cortical regions for both cognitively normal controls and stroke participants. Additionally, aphasia severity and lesion load had an effect on the cerebello-cerebral network connectivity in participants with aphasia. In a stepwise multiple linear regression, controlling for aphasia severity, time poststroke and lesion load, FC between the right Cereb2-left Cereb1 (standardized beta [std B]= -0.255, p < 0.004), right Cereb2-right anterior MTG (std B = 0.259, p < 0.004), and the right Cereb2-left anterior STG (std B = -0.208, p < 0.018) were significant predictors of BNT score. The overall model fit was R2 = 0.786 (p = 0.001). Conclusion: Functional connections between the right cerebellum and residual bilateral cerebral hemisphere regions may play a role in predicting naming ability in poststroke aphasia.

Functional status is associated with mean right atrial pressure derived from cardiac MRI

Abstract Background Right atrial pressure (RAP) is a key metric in haemodynamic assessment. Elevated RAP is associated with poor prognosis in heart failure and pulmonary hypertension and physiologically with fluid overload. RAP can be measured invasively or non-invasively, but Cardiac MRI (CMR) currently cannot estimate RAP. Purpose To develop a model to estimate RAP from CMR from paired right heart catheter (RHC) and CMR assessments. Assess correlation to WHO functional status prior to prospective clinical validation studies. Methods Patients were recruited to a registry of those referred for assessment of dyspnoea between 2012 and 2020. Inclusion criteria were age &amp;gt;18 years, signs and symptoms of heart failure and adequate CMR image quality. Patients diagnosed with pulmonary arterial hypertension were excluded. RHC and CMR were performed on the same day. CMR 2- and 4- chamber cines were used to measure chamber dimensions, strain, ejection fraction and stroke volume with operator reviewed AI contours in MASS research software. Pearson’s product-moment correlation coefficient (r ) was used to assess relationships between CMR metrics and invasive mRAP. Variables were then included in stepwise multiple linear regression models to predict mRAP. These were compared with receiver-operator curve analysis and DeLong’s test. Kruskal-Wallis test compared median mRAP between WHO functional class groups. Results The cohort of 672 patients was divided based upon invasive mRAP ≤ 8mmHg (44%) and mRAP &amp;gt; 8mmHg (56%). Higher mRAP was associated with increased age, male sex and a higher diastolic blood pressure but there was no difference in rates of heart failure types although the majority had preserved ejection fraction (51% vs 52%) with few having reduced ejection fraction (4.1% vs 8%). Right atrial (RA) dimensions, strain and ejection fraction had the strongest correlation with mRAP with moderate correlation to right ventricular (RV) measurements. Multivariable models contained simple RA dimensions (model 1), RA dimensions and strain (model 2), RA and RV dimensions (model 3) and RA end systolic volume corrected for body mass index and sex (model 4). All models had similar predictive capability (Figure 1 Panel A). RA end systolic volume was isolated in model 1 as the only variable required for mRAP prediction (coefficient = 0.06, p &amp;lt; 0.001). Using a threshold of mRAP &amp;gt;8mmHg ROC analysis demonstrated an area under the curve of 0.78 (95% Confidence interval 0.75 to 0.81) (Figure 1 Panel B). Worse WHO functional Class at time of assessment was significantly associated with increased CMR derived mRAP (P&amp;lt;0.001) (Figure 2). Conclusions mRAP can be estimated with moderate confidence from CMR RA end systolic volume. Limited available clinical data suggest functional status may be predicted by CMR derived mRAP. Further studies on wider populations are required to validate this model externally, refine it and determine its prognostic clinical significance.

Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation

Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly and accurately monitor the growth of reclamation vegetation, this study investigates the spectral variations and their impact on the accuracy of chlorophyll estimation, utilizing hyperspectral data and relative chlorophyll content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with the original spectra and first derivative spectra, to enhance spectral anomalies in the reclamation vegetation and identify chlorophyll-sensitive spectral features. Additionally, multiple linear stepwise regression and backpropagation neural network models were employed to estimate chlorophyll content. The results revealed the following: (1) the d5 and d6 scales of the discrete wavelet effectively highlighted spectral anomalies in the reclamation vegetation; (2) Salix japonica (Salix fragilis L.), among typical reclamation species, exhibited poor adaptability to the environmental conditions of the rare earth mining area; (3) the backpropagation neural network model demonstrated superior performance in chlorophyll estimation, with the spectral features Fir, Fir_d4, Fir_d5, and Fir_d6 significantly enhancing the accuracy of the model, achieving an R2 of 0.93 for Photinia glabra (Photinia glabra (Thunb.) Maxim.). The application of continuous–discrete wavelet transforms to hyperspectral data significantly improves the precision of chlorophyll estimation, underscoring the potential of this method for the rapid monitoring of reclamation vegetation growth.

How environmental data enables intelligent navigation: Factors assessment of affecting the navigation in the Northeast Arctic Route

Due to the more favorable navigational environment and higher safety along Northeast Arctic Route, this route is being used more and more frequently for international shipping. In this research, the factor analysis method is applied to assess the navigational environment of each sea area in the Northeast Arctic Route, revealing the effects of Arctic navigation influence factor on the navigational environment, and analyzing the change trend of the navigational environment of different sea areas in different seasons. Further, the effects of multiple factors on the number of voyages are discussed through multiple stepwise regression model, including the navigational environment score, geopolitical risk, export & import trade and the probability of pollution accidents, then the Interrupted time series analysis (ITSA) method combined with seasonal decomposition is explored the effect of emergencies on the number of voyages on the Northeast Arctic Route. The number of voyages on the Northeast Arctic Route has been altered to varying degrees by the occurrence of various emergencies. Further, the Yamal LNG project and the Russian-Ukrainian conflict have a significant effect on the number of voyages, while the Covid-19 epidemic has a lesser effect on the number of voyages.

Aortic root morphometry revisited-Clinical implications for aortic valve interventions.

The complex anatomy of the aortic root is of great importance for many surgical and transcatheter cardiac procedures. Therefore, the aim of this study was to provide a comprehensive morphological description of the nondiseased aortic root. We morphometrically examined 200 autopsied human adult hearts (22.0% females, 47.9 ± 17.7 years). A meticulous macroscopic analysis of aortic root anatomy was performed. The largest cross-section area of the aortic root was observed in coaptation center plane (653.9 ± 196.5 mm2), followed by tubular plane (427.7 ± 168.0 mm2) and basal ring (362.7 ± 159.1 mm2) (p < 0.001). The right coronary sinus was the largest (area: 234.3 ± 85.0 mm2), followed by noncoronary sinus (218.7 ± 74.8 mm2) and left coronary sinus (201.2 ± 78.08 mm2). The noncoronary sinus was the deepest, followed by right and left coronary sinus (16.4 ± 3.2 vs. 15.9 ± 3.1 vs. 14.9 ± 2.9 mm, p < 0.001). In 68.5% of hearts, the coaptation center was located near the aortic geometric center. The left coronary ostium was located 15.6 ± 3.8 mm above sinus bottom (within the sinus in 91.5% and above sinutubular junction in 8.5%), while for right coronary ostium, it was 16.2 ± 3.5 mm above (83.5% within sinus and 16.5% above). In general, males exhibited larger aortic valve dimensions than females. A multiple forward stepwise regression model showed that anthropometric variables might predict the size of coaptation center plane (age, sex, and heart weight; R2 = 31.8%), tubular plane (age and sex; R2 = 25.6%), and basal ring (age and sex; R2 = 16.9%). In conclusion, this study presents a comprehensive analysis of aortic-root morphometry and provides a platform for further research into the intricate interplay between structure and function of the aortic root.

Classification and regression tree (CART) for predicting cadmium (Cd) uptake by rice (Oryza sativa L.) and its application to derive soil Cd threshold based on field data

The entry of Cd into soil-rice systems is a growing concern as it can pose potential risks to public health. To derive regional soil Cd threshold, a total of 333 paired soil and rice samples was collected in Anhui Province, Eastern China. The results showed that the total soil Cd and soil Zn/Cd were the most significant variables contributing to Cd content in polished rice. The Chinese Soil Quality Standards might overestimate risk posed by Cd-contaminated soil for rice production in the mining area due to high Zn/Cd values of some mining-associated soils. Cd levels in polished rice can be predictable using stepwise multiple linear regression (MLR) model. However, the derived soil Cd threshold based on the MLR model would be unrealistically high. The classification and regression tree method (CART) performed well in simulating Cd levels in polished rice and can be used to derive soil Cd threshold instead of MLR to minimize the uncertainty.

The association of different acute manifestations of multiple sclerosis on functional outcome

Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system (CNS), typically presenting in young adults (20–50 years old). Clinical manifestations are heterogeneous, depending on which part of the CNS demyelination occurs. Therefore, this study aims to assess whether different symptoms at first acute manifestation of MS are associated with worse functional outcomes. We enrolled all patients with a confirmed diagnosis of MS, regardless of the subtype, so long as it fulfilled the McDonald's 2017 criteria. A stepwise multiple linear regression model included statistically significant (p &lt; 0.05) variables in the Mann–Whitney U test. A total of 195 patients with MS were included in the final analysis, of which 140 (78.5%) were female. Acute blurry vision, acute paralysis, acute hypoesthesia, autonomic syndrome, and Lhermitte's sign at disease outbreak were found to be associated with worse EDSS (Expanded Disability Status Scale) in univariate tests. In adjusted analysis, the independent predictors of worse EDSS were acute blurry vision (Beta = 0.183; p = 0.010) and autonomic syndrome (Beta = 0.219; p = 0.003). These results may help better understand the relationship between MS symptomatology, functionality, and patient prognosis, potentially assisting physicians in determining MS patient's initial treatment.

Microbial nitrogen cycling in Microcystis colonies and its contribution to nitrogen removal in eutrophic Lake Taihu, China

Cyanobacterial blooms induced by excessive loadings of nitrogen (N) and other nutrients are a severe ecological problem in aquatic ecosystems. Previous studies of N removal have primarily focused on sediment-water interface, yet the role of cyanobacterial colonies has recently been attracting more research attention. In this study, N cycling processes were quantified for cyanobacterial colonies (primarily Microcystis colonies) and their contribution to N removal was estimated for a large, shallow eutrophic lake in China, Lake Taihu. Various N cycling processes were determined via stable 15N isotope, together with 16S rRNA gene sequencing and quantitative microbial element cycling (QMEC) chip. Denitrification was found to be the most prominent process, estimated to be 36.63, 9.85, 3.35, and 3.15 times higher than dissimilatory nitrate reduction to ammonium (DNRA), nitrification, ammonium (NH4+) uptake and nitrate (NO3−) uptake rates, respectively. Denitrifiers accounted for a large part of the bacterial taxa (35.50 ± 24.65%), and the nirS gene was the most abundant among N cycling-related genes, with (2.54 ± 0.51) × 109 copies g−1Microcystis colonies. A field investigation revealed a positive correlation between the potential denitrification rate and the Chl-a concentration (mostly derived from Microcystis colonies). Based on a multiple stepwise regression model and historical data from 2007 to 2015 for Lake Taihu, the total amount of N removed via denitrification by Microcystis colonies was estimated at 171.72 ± 49.74 t yr−1; this suggests that Microcystis colonies have played an important role in N removal in Lake Taihu since the drinking water crisis in 2007. Overall, this study revealed the importance of denitrification within Microcystis colonies for N removal in eutrophic lakes, like Lake Taihu.

The Effects of Diabetes on Gas Transfer Capacity, Lung Volumes, Muscle Strength, and Cardio-pulmonary Responses During Exercise.

Diabetes is a risk factor for the development of vascular disease, chronic kidney disease, retinopathy, and neuropathy. Diabetes is a co-morbid condition commonly present in patients with respiratory disorders but the extent to which it influences ventilatory capacity, gas exchange, and functional capacity is not well known. Research question Does the presence of diabetes contribute to impairment in spirometry, gas transfer, and exercise capacity? Retrospective analysis of all subjects who performed incremental cardio-pulmonary exercise testing (CPET) between 1988 and 2012 at McMaster University Medical Centre. The impact of diabetes on physiological outcomes and maximum power output (MPO) was assessed using stepwise multiple additive linear regression models including age, height, weight, sex, muscle strength, and previous myocardial infarct as co-variates, and was also stratified based on BMI categories. 40,776 subjects were included in the analysis; 1938 (5%, 66% male) had diabetes. Diabetics were older (59 vs. 53 years), heavier (88.3 vs.78.0 kg), and had a higher BMI (31 vs. 27kg/m2). The presence of diabetes was independently associated with a reduction in FEV1 (-130ml), FVC (-220ml), DLCO (-1.52ml/min/mmHg), and VA (-340ml) but not KCO. Patients with diabetes achieved a lower % predicted MPO[diabetic subjects 70% predicted (670 kpm/min ± 95% CI 284) vs. 80% in non-diabetics (786 kpm/min ± 342), p < 0.001]. With the exception of KCO, these differences persisted across BMI categories and after adjusting for MI. The presence of diabetes is independently associated with weaker muscles, lower ventilatory and gas transfer capacity and translates to a lower exercise capacity. These differences are independent of age, height, weight, sex, and previous MI.

Income generation, guideline, SU Income Generating Guidelines for Souphanouvong University

The purpose of this research is to analyze determinants of the guideline for income generation of Souphanouvong University, by distributing a questionnaire to 215 employees, using descriptive statistics and reference statistics, using a stepwise multiple regression model The results found that the guidelines to generate income from activities are based on the budget income provided by the state in combination with the creation of short-term courses, providing policies for students, improving teaching manuals, providing external academic services, expanding cooperation, transforming into a service and research center, renting space. On the one hand, to generate revenue in terms of management are creating incentives, putting in place the management rules that are oriented, having a person in charge, the revenue management task to have a single window, and providing a clear budget to be used in each period. Allow time for staff to provide services - generate income that is not too limited, each board should have a detailed plan to generate income, provide modern teaching and learning facilities, turn existing assets into capital, invest to improve the environment of the university more and more.

Citizens' acceptance of prefabricated housing in China: the role of procedural justice and uncertainty

PurposeThis study explores citizens’ acceptance of prefabricated housing (PH) from the perspective of socio-demographic and psychological factors, aiming to reveal the formation of PH acceptance and realize a sustainable development of PH.Design/methodology/approachThis study proposed hypotheses drawing on procedural justice theory and uncertainty management theory. A survey of 245 respondents was conducted, and the collected data was analyzed in a stepwise multiple regression model. Then, the psychological influencing mechanism was demonstrated using a mediation model.FindingsResults of the data analysis manifested that citizens’ acceptance of PH was influenced by socio-demographic and psychological factors, where psychological factors had more significant effects on acceptance than socio-demographic factors. The psychological mechanism was examined by verifying the mediating role of uncertainty between procedural justice and the acceptance of PH. Furthermore, a scientific strategy for developing PH was proposed based on this empirical study.Originality/valueThis study extends the knowledge of procedural justice theory by investigating people’s acceptance in the PH context. This study is also one of the first studies to unveil the psychological mechanism toward a high-cost product with invisible technological innovation. This study contributes to the literature by introducing uncertainty management theory to a controversial issue, examining and expanding its application in a complicated context. Moreover, results highlight the positive influence of fair processes on controversial issues.

Dry season assessment of carbon storage and emission from upland and riparian soils in the Ganga River basin

Soil organic carbon (C) and nitrogen (N) are profoundly affected by changes in land use and land cover (LULC), especially by farming in riparian zones. The five LULC classes were selected in contiguous order, i.e., undisturbed forest (CALref), conventional agriculture lands (CAL), reservoir riparian zones (RSRZ), river riparian zones (RRZ), and undisturbed riparian zones (RZref) in the Ganga River basin to study the C storage and emission trends at a landscape scale. The riparian soils showed higher moisture, temperature, and bulk density than the upland soils, strongly determining the spatial variations in the CO2 equivalent (CO2e), C:N ratio, and CO2 efflux. Riparian CO2e was more bulk density-dependent, while upland CO2e showed greater dependence on TOC. The C:N ratio showed a higher mean value in the reference soils (CALref and RZref) than in the other soils (P < 0.05). The total nitrogen (TN), total protein (TP), and NH4-N (ammonium‑nitrogen) showed the following trend: RSRZ > CAL > RRZ > RZref > CALref. The stepwise multiple regression models illustrated that soil moisture was the primary regulator of the C:N ratio and CO2 efflux in the upland soils while the temperature in the riparian soils. These intrinsic soil variables resulted in 1.15 to 2.26 times higher CO2 efflux from the cultivated soils (CAL, RSRZ, and RRZ) than from the reference soils. Hence, the present study revealed how agricultural practices tangibly increase the riparian system's carbon footprint while offering unsustainable livelihood options to farmers.