Miracle-dz/UniFAR-FTUs huggingface dataset:
A Quantitative Study of the Relationship between Project Performance and BIM Use on Commercial Construction Projects in the USA

ABSTRACT This article reports the findings of a quantitative study on the relationship between various project performance outcomes and the use of Building Information Modeling (BIM), within the commercial construction industry. The literature review identified inconsistent results among prior qualitative and quantitative work concerning the relationships of key project performance measures and BIM use. Data on 13 variables from 93 completed construction projects were collected and examined through a causal comparative research design. Projects that used BIM (in design or construction) were not found to experience significant performance outcomes when controlling for the contribution of other independent variables and covariates at the 95% confidence level (CL). At the lower 90% CL, however, projects using BIM in construction experienced significantly higher levels of schedule growth than projects that did not use BIM in construction. Recommendations are made for training of construction management staff as a step to avoid unwanted schedule growth associated with BIM use during construction. This study is one of a very small group of rigorous quantitative analyses of BIM project performance outcomes conducted to date; additionally, it is the only study that has analyzed BIM in a multivariate context that controlled for the contribution of other project variables not directly related to BIM use.
Past, Present, and Future of Corporate Social Responsibility and Earnings Management Research

For decades, corporate social responsibility (CSR) has been an area at the heart of business research. The aspects of CSR that researchers have been focusing on have grown exponentially. Most research efforts have dwelled on its positive effects, including good behavioural changes, promising financial performance, social investment and development, and environmental preservation and protection. On the other hand, several scholars have questioned the credibility of CSR and its link to unethical financial conduct, particularly earnings management. This review paper uses a systematic literature review to conduct bibliometric analysis, content analysis, and thematic analysis in order to identify trends and outcomes of CSR reporting and earnings management research. The literature review search is guided by PRISMA and analyses 152 Scopus-indexed articles between 2005 and 2021. As a result, this paper reveals the most influential authors and articles, leading journals and countries, and research cluster networks. This review also outlines the theoretical perspectives and future direction of CSR and earnings management research. In addition, this review paper provides critical and beneficial insights for stakeholders and researchers.
Introducing a merged precipitation satellite model using satellite precipitation products, land surface temperature, and precipitable water vapor

Abstract Satellite precipitation have a lot of uncertainty and the spatial resolution is still quite coarse. Hence, the purpose of this study is to introduce a new approach based on multivariate linear modelling and Artificial Neural Networks (ANN) for the first time using four multisource satellite precipitation products and some other satellite data. Four different satellite precipitation products with different algorithms, along with Precipitable Water Vapor (PWV) and Land Surface Temperature (LST) data on a daily scale were used (to benefit interactions of atmospheric content). Five linear merge models, including five ANN models were introduced and calibrated using harmony search algorithm. The performance of the models was evaluated over Iran using observational rainfall data of 2014 to 2020. The findings of this study highlighted that the model based on ANN, which merged four rainfall products together with LST and PWV has outperformed individual satellite products and other presented models.
Identification of the Principle of Taste Sensors to Detect Non-Charged Bitter Substances by 1H-NMR Measurement

A taste sensor with lipid/polymer membranes is attracting attention as a method to evaluate taste objectively. However, due to the characteristic of detecting taste by changes in membrane potential, taste sensors cannot measure non-charged bitter substances. Many foods and medicines contain non-charged bitter substances, and it is necessary to quantify these tastes with sensors. Therefore, we have been developing taste sensors to detect bitter tastes caused by non-charged substances such as caffeine. In previous studies, a sensor for detecting bitterness caused by caffeine and theobromine, theophylline, was developed, using a membrane modified with hydroxybenzoic acid (HBA) as the sensing part. The sensor was designed to form intramolecular hydrogen bonds (H-bonds) between the hydroxy group and carboxy group of HBA and to successively cause the intermolecular H-bonds between HBA and caffeine molecules to be measured. However, whether this sensing principle is correct or not cannot be confirmed from the results of taste sensor measurements. Therefore, in this study, we explored the interaction between HBA and caffeine by 1H-nuclear magnetic resonance spectroscopy (NMR). By the 1H NMR detection, we confirmed that both the substances interact with each other. Furthermore, the nuclear Overhauser effect (NOE) of intermolecular spatial conformation in solution was measured, by which 2,6-dihydroxybenzoic acid (2,6-DHBA) preferably interacted with caffeine via the H-bonding and stacking configuration between aromatic rings. Identifying the binding form of 2,6-DHBA to caffeine was estimated to predict how the two substances interact.
Effects of Teff-Based Sourdoughs on Dough Rheology and Gluten-Free Bread Quality

Production of gluten-free bread (GFB) with good quality characteristics represents a technological challenge. Our study aimed to obtain nongluten bread from cereals and pseudocereals with applying single cultures of Pediococcus acidilactici, Pediococcus pentosaceus and Enteroccocus durans as sourdoughs. The effect of sourdoughs on the quality traits of gluten-free (GF) dough and GFB was explored. The structural and baking properties of GF dough composed of teff, rice, corn, and sorghum flours were improved by adding xanthan gum (0.6%), guar gum (1.0%) and carboxymethyl cellulose (1.0%). The tested strains reached 108 cfu/g in teff flour and produced sourdoughs with a pleasant lactic aroma. The sourdough-fermented doughs were softer and more elastic compared to control dough and yielded reduced baking loss. Strain Enterococcus durans ensured the best baking characteristics of GF dough and the highest softness of the GFB during storage. Strain Pediococcus pentosaceus had the most pronounced positive effect on aroma, taste and aftertaste. Pan baking was found to be more appropriate to obtain stable shape and good-looking products. A careful starter culture selection is necessary for GFB development since a significant effect of strain specificity on dough rheology and baking characteristics was observed.
Accuracy of Gram-stained Smears as Screening Tests for Neisseria gonorrhoeae: A Systematic Review and Meta-analysis

Background and Objective: A total of 86.9 million persons worldwide are infected with Neisseria gonorrhoeae (Ng). Although Gram-stained smears (GSS) provide a timeand cost-saving alternative to conventional laboratory tests, their global uptake partly depends on their performance. This study aimed to meta-analyze the diagnostic accuracy of GSS to screen for Ng. Materials and Methods: A literature search was conducted using the MEDLINE (1980 to 2020). Studies were included if they employed GSS to detect Ng in humans and compared the results with reference tests. Results: Eleven studies were reviewed and meta-analyzed and stratified by specimen type (Gram-stained urethral smears and Gram-stained endocervical, urethral swabs and urine smears.) and reference test type (culture method or NAAT). Sensitivity was similarly high in GSS versus NAAT (93% [CI, 64% to 99 %]) and GSS versus culture methods (87% [CI, 74% to 94%]), followed by Gram-stained urethral smears (97% [95% CI, 86% to 100%]) and Gram-stained endocervical, urethral swabs and urine smears (81% [CI, 67% to 90%]). Specificity was also high in GSS versus culture methods (98% [CI, 95% to 100%]) and GSS versus NAAT (94% [CI, 73% to 99%]), followed Gram-stained endocervical, urethral swabs and urine smears (98% [CI, 93% to 99%]) and Gram-stained urethral smears (96% [CI, 78% to 99%]). Conclusions: Data suggest that GSS have the highest accuracy when investigated against reference culture methods, and Gram-stained urethral smears have the highest accuracy, followed by Gram-stained endocervical, urethral swabs and urine smears. Given their accuracy, convenience, and quick turnaround time, GSS may be useful in expanding first-line screening Ng.
Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System

The coronavirus disease 2019 (COVID-19) has caused millions of deaths and one of the greatest health crises of all time. In this disease, one of the most important aspects is the early detection of the infection to avoid the spread. In addition to this, it is essential to know how the disease progresses in patients, to improve patient care. This contribution presents a novel method based on a hierarchical intelligent system, that analyzes the application of deep learning models to detect and classify patients with COVID-19 using both X-ray and chest computed tomography (CT). The methodology was divided into three phases, the first being the detection of whether or not a patient suffers from COVID-19, the second step being the evaluation of the percentage of infection of this disease and the final phase is to classify the patients according to their severity. Stratification of patients suffering from COVID-19 according to their severity using automatic systems based on machine learning on medical images (especially X-ray and CT of the lungs) provides a powerful tool to help medical experts in decision making. In this article, a new contribution is made to a stratification system with three severity levels (mild, moderate and severe) using a novel histogram database (which defines how the infection is in the different CT slices for a patient suffering from COVID-19). The first two phases use CNN Densenet-161 pre-trained models, and the last uses SVM with LDA supervised learning algorithms as classification models. The initial stage detects the presence of COVID-19 through X-ray multi-class (COVID-19 vs. No-Findings vs. Pneumonia) and the results obtained for accuracy, precision, recall, and F1-score values are 88%, 91%, 87%, and 89%, respectively. The following stage manifested the percentage of COVID-19 infection in the slices of the CT-scans for a patient and the results in the metrics evaluation are 0.95 in Pearson Correlation coefficient, 5.14 in MAE and 8.47 in RMSE. The last stage finally classifies a patient in three degrees of severity as a function of global infection of the lungs and the results achieved are 95% accurate.
CTTE: Customized Travel Time Estimation via Mobile Crowdsensing

Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy, but they largely rely on GPS trajectories which are too coarse to model many personalized driving behaviors, e.g., differentiating novice and veteran drivers. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS trajectories, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a road link traffic database with topology representation, speed statistics, and query distribution. It also calibrates inertial readings, estimates the arbitrary phone’s pose in car, and detects multiple aggressive driving events (e.g., bump judders, sharp turns, sharp slopes, frequent lane shifts, overspeeds, and sudden brakes). Finally, we demonstrate our solution on two typical transportation problems, i.e., predicting traffic speed at holistic level and estimating customized travel time at personal level, within a multi-task learning structure. Experiments on two large-scale real-world traffic datasets from DiDi platform show our effectiveness compared with the state-of-the-art.
Age‐specific, population‐level pedigree of wild black bears provides insights into reproduction, paternity, and maternal effects on offspring apparent survival

Abstract Wildlife pedigrees provide insights into ecological and evolutionary processes. DNA obtained from noninvasively collected hair is often used to determine individual identities for pedigrees and other genetic analyses. However, detection rates associated with some noninvasive DNA studies can be relatively low, and genetic data do not provide information on individual birth year. Supplementing hair DNA stations with video cameras should increase the individual detection rate, assuming accurate identification of individuals via video data. Video data can also provide birth year information for individuals captured as young of the year, which can enrich population‐level pedigrees. We placed video cameras at hair stations and combined genetic and video data to reconstruct an age‐specific, population‐level pedigree of wild black bears during 2010–2020. Combining individual birth year with mother–offspring relatedness, we also estimated litter size, interlitter interval, primiparity, and fecundity. We used the Cormack‐Jolly‐Seber model in Program Mark to evaluate the effect of maternal identity on offspring apparent survival. We compared model rankings of apparent survival and parameter estimates based on combined genetic and video data with those based on only genetic data. We observed 42 mother–offspring relationships. Of these, 21 (50%) would not have been detected had we used hair DNA alone. Moreover, video data allowed for the cub and yearling age classes to be determined. Mean annual fecundity was 0.42 (95% CI: 0.27, 0.56). Maternal identity influenced offspring apparent survival, where offspring of one mother experienced significantly lower apparent survival (0.39; SE = 0.15) than that of offspring of four other mothers (0.89–1.00; SE = 0.00–0.06). We video‐documented cub abandonment by the mother whose offspring experienced low apparent survival, indicating individual behaviors (e.g., maternal care) may scale up to affect population‐level parameters (e.g., cub survival). Our findings provide insights into evolutionary processes and are broadly relevant to wildlife ecology and conservation.
Reducing the Damage of Ox-LDL/LOX-1 Pathway to Vascular Endothelial Barrier Can Inhibit Atherosclerosis

Aim The destruction of the vascular endothelial barrier mediated by Ox-LDL is the initial link to atherosclerosis. Here, we aimed to determine whether the immunological intervention with Ox-ApoB polypeptide fragment (Ox-ApoB-PF) can block the deposition of Ox-LDL in vascular endothelial cells through LOX-1 receptors, thereby protecting the barrier function and survival status of vascular endothelial cells and inhibiting the progression of atherosclerosis. Methods and Results In order to determine the harm of Ox-LDL to vascular endothelial cells and the protective effect of immune intervention with Ox-ApoB-PF, we conducted a series of corresponding experiments in vitro and in vivo. The in vitro results showed that Ox-LDL can activate endothelial cell apoptosis pathway; reduce the expression of endothelial junction proteins; affect the migration, deformation, and forming ability; and ultimately destroy the vascular endothelial barrier function. The increased permeability of endothelial cells led to a sharp increase in the phagocytosis of Ox-LDL by macrophages under the endothelial layer. Meanwhile, Ox-LDL stimulation induced a significant upregulation of LOX-1 in endothelial cells and increased the expression of endothelial cell chemokines and adhesion factors. Ox-ApoB-PF antibodies can significantly reduce the abovementioned harmful effects. The in vivo results showed that active immune intervention through Ox-ApoB-PF can protect the endothelial barrier function; reduce macrophage deposition and the inflammatory response in plaques; alleviate lipid deposition in the plaques, as well as apoptosis and necrosis; and increase the ability of liver macrophages to clear Ox-LDL. Eventually, the progression of plaque and the formation of necrotic cores in plaques can be inhibited. Conclusions An Ox-ApoB-PF antibody may protect the endothelial cell physiological function and survival status by blocking the combination of Ox-LDL/LOX-1 in vascular endothelial cells. Immune intervention with Ox-ApoB-PF inhibits the occurrence and development of atherosclerotic lesions by protecting the vascular endothelial barrier function.
Association of Maternal Longitudinal Hemoglobin with Small for Gestational Age during Pregnancy: A Prospective Cohort Study

Background: Few studies have investigated the association of maternal longitudinal hemoglobin (Hb) with small for gestational age during pregnancy. The current study examined the associations of maternal Hb concentrations and Hb changes throughout the middle and late stages of pregnancy with small for gestational age (SGA) in a large prospective cohort study. Methods: This was a prospective cohort study, which enrolled pregnant women at 8–16 weeks of gestation and followed up regularly. Maternal Hb concentrations were measured at the middle (14–27 weeks) and late (28–42 weeks) stages of pregnancy, and the Hb change from the middle to late stage of pregnancy was assessed. The Log-Poisson regression model was used to identify the association of maternal Hb with SGA, including the implications of Hb during specific pregnancy periods and Hb change across the middle to late stages of pregnancy. Of the total 3233 singleton live births, 208 (6.4%) were SGA. After adjusting for potential confounders, compared with Hb 110–119 g/L, Hb ≥ 130 g/L at late pregnancy was significantly associated with a higher risk of SGA (adjusted RR: 2.16; 95% CI: 1.49, 3.13). When Hb changes from the middle to late stages of pregnancy were classified by tertiles, the greatest change in the Hb group (<−6.0 g/L) was significantly associated with a lower risk of SGA (adjusted RR: 0.56; 95% CI: 0.37, 0.85) compared with the intermediate group (−6.0~1.9 g/L). In conclusion, for women at low risk of iron deficiency, both higher Hb concentrations in late pregnancy and less Hb reduction during pregnancy were associated with an increased risk of SGA.
Fire Management and Carbon Programs: A Systematic Literature Review and Case Study Analysis

Abstract Fire management influences the global carbon balance. This systematic literature review of proactive fire management programs as nature-based climate solutions synthesizes findings into four case studies to bring insight to these programs and their additional benefits. We review 136 papers, mostly from Australia, Brazil, Southern Africa, and the USA, reflecting the relevance of proactive fire management to savanna ecosystems. Four themes emerged: governance and policies; methodologies; reduced wildfire and GHG emissions; and co-benefits. Australia provides most literature on methodologies to account for greenhouse gas emissions abated. Australia and the USA focus on the social co-benefits, typically among Indigenous peoples. Biodiversity outcomes are largely discussed in Brazilian and African studies. Evidence of a shift to proactive fire management is documented in the case studies analyzed, but political, technical, and cultural barriers prevent further implementation at broader scales.
Impact of Autonomy Support on the Association between Personal Control, Healthy Behaviors, and Psychological Well-Being among Patients with Hypertension and Cardiovascular Comorbidities

A sense of control and autonomy are key components in guiding health-related behaviors and quality of life in people with chronic diseases. This study investigated whether autonomy support from health professionals moderates the impact of personal control on psychological well-being through healthy behaviors in patients with hypertension and cardiovascular comorbidities. Data from 149 hypertensive patients with comorbid cardiovascular risk factors were collected via self-administered surveys. A moderated mediation effect of a hypothesized model was analyzed using the PROCESS macro bootstrapping method. Autonomy support from health professionals moderated the relationship between personal control and healthy behaviors (B = 0.16, t = 2.48, p < 0.05), showing that the effect of personal control on healthy behaviors differed by the level of autonomy support. Additionally, autonomy support moderated the mediation effect of healthy behaviors in the relationship between personal control and psychological well-being (Index = 0.15; 95% CI = 0.010, 0.335). The mediation effect existed only in patients with higher autonomy support. The findings demonstrate that autonomy support from health professionals plays a crucial role in reinforcing the positive impact of personal control on healthy behaviors and psychological well-being. Enhancing the supportive attitudes of health professionals that facilitate patients’ autonomous self-regulation is necessary for better health outcomes in people with combined cardiovascular diseases.
The Impact of Mentoring in the Development of Pre-Service Teachers from a University in South Africa

Limited conceptual knowledge, and the lack of competencies and skills in the discharge of duties have been part of the challenges faced by some teachers within the South African school system. One of the reasons attributed to this is poor teacher development programmes in some of the training institutions. This study explored the impact of mentorship development on pre-service teachers from a university in South Africa. The five-factor model of mentoring namely, personal attributes, system requirements, pedagogical knowledge, modelling, and feedback was employed as a theoretical lens. The purpose was to understand the attributes and practices mentors transfer to their mentees (student teachers) during their teaching practice. Located in the interpretive paradigm, the research employed a qualitative case study design to generate data through semi-structured interviews from 26 B.Ed Honours pre-service teachers, purposively selected as the study sample. A thematic approach was used to analyse the data generated. Findings from the analyses revealed that pre-service teachers were positively impacted mostly in the development of pedagogical knowledge, followed by system requirements, personal attributes, modelling and feedback. The findings support the Five-Factor mentoring Model as a valid and useful framework for measuring the impact of the mentoring received by pre-service teachers on teaching practicum. The study concludes that mentoring activity in pre-service teacher education is an important component of professional development for effective teachers. Based on this conclusion, the study recommends that mentors support student teachers in the development of the necessary skills and competencies required of a professional teacher.