bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantum-level machine learning calculations to predict the PES of Selegiline
Authors: Shirani Hossein , Hashemianzadeh Seyed Majid 
Published: February 2024

Abstract
Many drug molecules contain functional groups, resulting in a torsional barrier corresponding to rotation around the bond linking the fragments. In medicinal chemistry and pharmaceutical sciences, inclusive of drug design studies, the exact calculation of the potential energy surface of these molecular torsions is extremely important and precious. Machine learning, including deep learning, is currently one of the most rapidly evolving tools in computer-aided drug discovery and molecular simulations. In this work, we used ANI-1x neural network potential as a quantum-level machine learning to predict the PESs of the Selegiline antiparkinsonian drug molecule. Also, DFT calculations at the wB97X/6-31G(d) level of theory have been used to study the structural parameters and vibrational normal modes of the Selegiline molecule. We succeeded in calculating the vibrational frequencies, electronic energy and optimization of the molecular structure of the Selegiline using the ANI-1x dataset in a very short computing cost. From this perspective, we expect the ANI-1x dataset applied in this work to be appreciably efficient and effective in computational structure-based drug design studies.
Ceftazidime-Avibactam Use in Children Admitted in Pediatric Intensive Care Units
Authors: SILVA ANDRE RICARDO ARAUJO DA , QUIJADA RAFAEL 
Published: February 2024

Abstract
Purpose: Ceftazidime-Avibactam (CAZ-AVI) is one of the new antibiotics available to treat infections due to carbapenem-resistant Gram-negative bacteria. Our aim was to describe CAZ-AVI treatments in children admitted in pediatric intensive care units (PICUs).
Methods: We conducted a retrospective descriptive study in two PICUS of Rio de Janeiro city, Brazil, between January 2020 and January 2024. We included children between 0 and 18 years that used CAZ-AVI for more than 24 hours. Duration of CAZ-AVI therapy, previous healthcare-associated infections and carbapenem use, length of stay and outcomes in 30 days. All treatments were previously discussed with an infectious disease specialist.
Results: CAZ-AVI was used in 37 patients. Median of age was 28 months, 17 (45.9%) were male. Median time from admission until the initial presciption of CAZ-AVI was 39.9 days (variation between 1-138 days). Thirty-four (91.9%) children had at least one comorbidity at admission, 34/37 (91.9%) used at least one invasive device previously CAZ-AVI prescription, 33/37 (89.2%) used carbapenem before and 15 (40.5%) had a HAI before CAZ-AVI use. The mean time of use was 11 days (variation 1 to 22 days). Gram-negative bacteria were isolated in cultures of 12/37 (32.4%) patients, in the 24h before presciption or on the day of prescription. Mortality in 30 days was 14/37 (37.8%).
Conclusion: We concluded that almost all patients that used CAZ-AVI were critically ill children, with multiple comorbidities, previous use of carbapenem and high rate of mortality.
Chemical surface densification of sugar maple through Michael addition reaction
Authors: Akbari Vahideh , Vanslambrouck Stéphanie , Landry Veronic 
Published: February 2024

Abstract

Wood densification is a technique to enhance wood density and hardness, presenting a promising solution to expand wood use across various applications. However, current densification methods have cost and environmental impact limitations. This project introduces a potential environmentally friendly approach involving surface chemical densification through in-situ polymerization, using carbon Michael addition reaction between biobased acrylate and malonate monomers. This reaction, conducted in mild conditions with low energy and solvent consumption, aims to enhance wood densification while minimizing environmental impact. Various malonate-acrylate systems were formulated, and were optimized based on their viscosity, conversion rate, glass transition temperature, crosslinking density, and hardness. Then, sugar maple wood samples were densified with the most effective formulations. Monomers with lower viscosity demonstrated higher level of chemical retention. Density profile and penetration depth were also higher for the samples impregnated with lower viscosity formulations, as confirmed by X-Rray densitometer and scanning electron microscopy. Confocal Raman spectroscopy confirmed that formulations successfully filled lumens and vessels without reacting with the cell wall components. The brinell hardness was used to determine the hardness of natural and densified woods. One-way ANOVA data analysis showed a significant increase in hardness of densified samples compared to untreated wood; however, based on TUKEY Anova analysis, no noticeable difference was reported between impregnated samples with different formulations. Overall, results showed the potential effectiveness of the Michael addition reaction in wood impregnation.
Fourteen-Year Creep Monitoring and Benchmark Velocity Parameters of Natural Slopes at ‘Serra do Mar’, Brazil
Authors: Abramento Mauricio , Leite Rafael Brandão 
Published: February 2024

Abstract

This paper presents the analysis and results of a 14-year monitoring of creep behavior along a 100m high slope at Serra do Mar, Brazil. The slope is located nearby a roadway and an industrial area and was suffering from creep movements triggered by an excavation at its foot. Creep movements were especially severe during the rainy periods due to water table fluctuation. Inclinometer readings from 2009 to 2011 showed that the creep movement involved a soil mass around 15 to 20m thick and was in the so-called tertiary phase, with relatively high acceleration. Prediction models showed that the slope failure would probably occur in another two to three years, which required immediate implementation of mitigation actions. By the end of 2011, several horizontal deep drains were installed along the slope to reduce the water table level. Since then, the inclinometers showed that creep acceleration was eliminated and creep velocity was substantially reduced, bringing the slope back to primary and secondary, stable creep movements. Monitoring results of Horizontal Deep Drains show that flow volumes increase substantially during the rainy seasons, showing that the solution efficiently stabilizes the slope. With the basis of monitoring results for both secondary and tertiary creep phases as well as comparison with instrumentation results from other monitored slopes in the region, benchmark parameters related to creep velocity and acceleration for Serra do Mar slopes are discussed and presented. This constitutes the first organized study on slope movement velocities at Serra do Mar and presents an important contribution for researchers and designers.
Product Insights from Customer-Generated Data Using Topic Modeling with BERTopic and Sentiment Analysis with XLM-T: An Experiment on Turkish Reviews
Authors: Birim Şule Öztürk 
Published: February 2024

Abstract
As information sharing through social media becomes widespread in every field, users frequently share their experiences with products purchased through e-commerce sites. This user-generated content is an opportunity for product owners to monitor users’ opinions. Since the number of user reviews is ever-increasing, decision makers need the right methods to monitor and extract valuable information from review data. In this study, an approach is proposed to determine the most prevalent product aspects and users’ opinions about them. In the proposed approach, first-topic modeling is applied to extract mostly debated product features. In addition, the monthly changes in the topics of reviews over time were examined using dynamic topic modeling. Next, sentiment analysis is applied to identify whether the customers like or dislike the features in the extracted topics. To apply the proposed approach, reviews about six similar security cameras were scraped from HepsiBurada.com, a famous e-commerce platform in Turkey. BERTopic is applied to extract topics, while XLM-T, a transformer-based technique, is implemented for sentiment analysis. From the experiments, fourteen topics related to product features were found. Extracted topics are mostly debated during the pandemic period. After the pandemic, the frequencies relatively stabilized. Among the extracted topics, ten have positive sentiment, while four have negative sentiment. The amount of review data is limited, and a different product with a large number of reviews can be used for further studies. Topics are manually named by exploring representative words. Further studies can utilize a tool for the automated labeling of topics using representative words. Sentiments about the product features may present valuable insight into product improvement scenarios. Additionally, the proposed approach can systematically identify product opportunities from a large amount of user-generated data.
Qualitative study on endogenous dynamic mechanism of drug dependence
Authors: Wang Jingsong , wang shen , gan xiaohuan 
Published: February 2024

Abstract
Stimulating and strengthening the endogenous motivation of abstinence is the key and prerequisite for the psychological withdrawal of synthetic drug addicts.At present, there is still a lack of systematic explanation framework for the endogenous motivation of abstinence.The article takes endogenous motivation as the starting point, combines endogenous and exogenous two-way perspectives, and based on in-depth interviews, the grounded theory research method, adopts the intensity sampling strategy, and takes typical drug addicts, successful drug addicts, and front-line policemen in drug rehabilitation centers under the jurisdiction of Fujian Province as the research objects to build an overall endogenous motivation resource exhaustion model for drug addicts.The research shows that: (1) the "four factor onion model" with lack of control, negative sense of life meaning, negative goal pursuit, passive cutting and expectation as the main body is the negative internal driving force for drug addicts to quit addiction.(2) The lack of sense of control is an important inducement for the lack of endogenous motivation to quit addiction, and the lack of external supervision of withdrawal mechanism may be one of the external reasons for the lack of sense of control.(3) The gradual improvement of family internal construction capacity and social support system can stimulate the endogenous motivation of abstinence and promote the reconstruction behavior of abstinence individuals.
An overview of wastewater treatment technologies in Minas Gerais, Brazil: predominance of anaerobic reactors
Authors: Muniz de Queiroz Marina , Dantas Marina Salim , Oliveira Sílvia Maria Alves Corrêa 
Published: February 2024

Abstract

The deficit in sewage collection and treatment is a problem in Brazil, and a large portion of untreated sewage, usually disposed of directly into water bodies, compromises water quality, with implications for public health and environmental balance. This scenario does not differ in the State of Minas Gerais, where only 44% of the urban population has their sewage treated. This study aimed to evaluate the current use of wastewater treatment technologies in active wastewater treatment plants (WWTPs) in Minas Gerais, based on a database from the Sewage Atlas, and analyze how the characteristics of the municipality and the treatment system influence the selection of adopted technologies. Information on 376 active WWTPs and 26 WWTPs designed or under construction was obtained. The most used technologies include upflow anaerobic sludge blanket (UASB) reactors, with or without post-treatment, adopted in more than 70% of WWTPs. A logistic regression model was fitted to explain the presence of post-treatment UASB reactors in the municipality. The model showed that the chance of implementing this technology is 3.3 times greater for municipal autarchies and 7.1 times greater for public companies compared to municipal governments.
Effect of exposure to disinfection by-products during swimming exercise on asthma-related immune responses
Authors: Lee Bo-Ae 
Published: February 2024

Abstract
Swimming is a widely practiced exercise in modern society, where there is a heightened interest in health. The exceptional benefits of swimming are well-known, yet the issue of water quality management inevitably arises due to its nature as an aquatic exercise. Several studies reported that chlorine disinfectants commonly used in swimming pool water disinfection could degrade into toxic disinfection by-products (DBPs) and suggested that the DBPs might induce respiratory disorders, including asthma. Conversely, there were also reports that the DBPs had no significant effects on respiratory conditions. In this study, we investigated the influence of swimming exercise and DBPs on asthma. The decomposition products had little effect on the number of T cells in various immune organs. However, swimming exercise was found to increase the cell count in proportion to the exercise duration. Nevertheless, there were no significant changes in other immune cells and the secretion of asthma-related cytokines. These findings indicate that the effects of swimming pool DBPs on respiratory conditions during swimming exercise are either negligible or absent, and instead, the immunological benefits gained through consistent swimming exercise outweigh any potential drawbacks.
Integrated forecasting method of medium-and long-term runoff by ridge regression based on optimal sub-model selection
Authors: Chen Binbin , Chen Zhengdong , Song Chuping , Song Yanhong 
Published: February 2024

Abstract

Numerous studies have demonstrated that the combination models can improve the runoff forecast performance compared to individual forecasts. However, some models do not take into account the effects of inappropriate sub-models on the combination models. Based on this, a medium-and long-term runoff integrated forecasting method based on optimal sub-models selection was proposed. First, the sub-models, including linear regression (MLR), BP neural network (BPNN), wavelet neural network (WNN), and support vector regression (SVR), are optimally selected based on the nearness degree. Second, ridge regression (RR) is used to combine the optimal sub-models to predict runoff. Finally, the Guandi hydropower station is taken as an example to verify the effect of the integrated forecasting model. The results show that SVR, BPNN, and WNN are the optimal sub-models, and RR-3 is the optimal integrated forecasting model composed of the optimal sub-models. In addition, compared with the other two combination models, the RR-3 performs better.
Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization
Authors: Hajinajaf Shayan , Jolandan Shaban Ghavami , Masoudi Hassan , Rohani Abbas 
Published: February 2024

Abstract

Water scarcity is recognized as a critical global concern and one viable solution involves extracting water from atmospheric humidity by leveraging subterranean coldness. This study meticulously evaluates the operational efficacy of a water production system by examining four pivotal factors: buried pipe length (TL), air flow rate (AFR), air temperature (AT), and air humidity (AH). A positive correlation between these variables and water vapor production is established, with AT exerting the most pronounced influence. Significantly, the analysis of variance reveals the main and interactive effects of the variables, except for TL × AFR, at a 5% significance level. To enhance understanding of the intricate interplay among these factors, a proficient least squares support vector machines model is devised, employing a radial basis function kernel. This model demonstrates an impressive 98% concurrence between projected and empirical data, with a minimal error of 0.66 mL and 5.99%. An in-depth sensitivity analysis underscores the differential impact of AT, AH, TL, and AFR on water vapor (WV) prediction. Optimal values of 3.98 m, 6.89 m3/h, 46.30 °C, and 86.62% for TL, AFR, AT, and AH, respectively, are obtained through subsequent optimization of independent variables using genetic algorithms, resulting in a notable water production of 23.61 mL.
Nitrate increases the capacity of an aerobic moving-bed biofilm reactor (MBBR) for winery wastewater treatment
Authors: Walker Patricio , Nerenberg Robert , Pizarro Gonzalo , Aybar Marcelo , Pavissich Juan Pablo , González Bernardo , Pastén Pablo 
Published: February 2024

Abstract

We used bench-scale tests and mathematical modeling to explore chemical oxygen demand (COD) removal rates in a moving-bed biofilm reactor (MBBR) for winery wastewater treatment, using either urea or nitrate as a nitrogen source. With urea addition, the COD removal fluxes ranged from 34 to 45 gCOD/m2-d. However, when nitrate was added, fluxes increased up to 65 gCOD/m2-d, twice the amount reported for aerobic biofilms for winery wastewater treatment. A one-dimensional biofilm model, calibrated with data from respirometric tests, accurately captured the experimental results. Both experimental and modelling results suggest that nitrate significantly increased MBBR capacity by stimulating COD oxidation in the deeper, oxygen-limited regions of the biofilm. Our research suggests that the addition of nitrate, or other energetic and broadly used electron acceptors, may provide a cost-effective means of covering peak COD loads in biofilm processes for winery or another industrial wastewater treatment.
Virtual Reality–Based Training in Chronic Low Back Pain: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Li Ran , Li Yinghao , Kong Youli , Li Hanbin , Hu Danrong , Fu Chenying , Wei Quan 
Published: February 2024

Background
Low back pain is one of the most prevalent pain conditions worldwide. Virtual reality–based training has been used for low back pain as a new treatment strategy. Present evidence indicated that the effectiveness of virtual reality–based training for people with chronic low back pain is inconclusive.

Objective
This study conducted a meta-analysis to evaluate the immediate- and short-term effects of virtual reality–based training on pain, pain-related fear, and disability in people with chronic low back pain.

Methods
We searched the PubMed, Embase, Web of Science, PEDro, CENTRAL, and CINAHL databases from inception until January 2024. Only randomized controlled trials assessing the effects of virtual reality–based training on individuals with chronic low back pain were selected. The outcomes were focused on pain, pain-related fear measured by the Tampa Scale of Kinesiophobia, and disability measured by the Oswestry Disability Index. The immediate term was defined as the immediate period after intervention, and the short term was defined as 3 to 6 months after intervention. The Cochrane Risk of Bias tool and the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach were used to evaluate the quality of the methodology and evidence, respectively.

Results
In total, 20 randomized controlled trials involving 1059 patients were eligible for analysis. Virtual reality–based training showed significant improvements in pain (mean difference [MD] –1.43; 95% CI –1.86 to –1.00; I2=95%; P<.001), pain-related fear using the Tampa Scale of Kinesiophobia (MD –5.46; 95% CI –9.40 to 1.52; I2=90%; P=.007), and disability using the Oswestry Disability Index (MD –11.50; 95% CI –20.00 to –3.01; I2=95%; P=.008) in individuals with chronic low back pain immediately after interventions. However, there were no significant differences observed in pain (P=.16), pain-related fear (P=.10), and disability (P=.43) in the short term.

Conclusions
These findings indicated that virtual reality–based training can be used effectively for individuals with chronic low back pain in the immediate term, especially to reduce pain, alleviate pain-related fear, and improve disability. However, the short-term benefits need more high-quality trials to be demonstrated.

Trial Registration
PROSPERO CRD42021292633; http://tinyurl.com/25mydxpz
Help-Seeking, Support, and Engagement in Gestational Diabetes Mellitus Online Communities on Facebook: Content Analysis
Authors: Pham Sheila , Churruca Kate , Ellis Louise A , Braithwaite Jeffrey 
Published: February 2024

Background
The prevalence of gestational diabetes mellitus (GDM) has drastically risen in recent years. For some, self-management includes the use of GDM online communities on Facebook. Such communities can fill gaps in information and support that participants are not able to access elsewhere to address unmet needs. Given the popularity of sharing information about pregnancy on Facebook and the documented benefits of diabetes online communities, the same may be true of GDM online communities.

Objective
This study aimed to categorize and quantify what is being discussed in GDM Facebook groups, including informational and emotional help-seeking behavior, and how this support and engagement may be demonstrated by peers through comments and reactions.

Methods
We sourced the data from the 2 largest Facebook groups focused on GDM in Australia. A summative content analysis was conducted on original posts across the 2 groups and coded for topics as well as help-seeking types. The coding scheme was based on the previous work of Liang and Scammon. Visible indicators of engagement, including the number of comments and “reactions,” were tabled and manually evaluated.

Results
There were 388 original posts, and the analysis produced 6 topics: GDM self-management (199/388, 51.3%), GDM clinical management (120/388, 30.9%), preparing for birth (40/388, 10.3%), mental distress (35/388, 9%), birth announcement (29/388, 7.5%), and GDM journey reflections (21/388, 5.4%). Secondary coding of help-seeking type revealed more than half of the posts were informational help-seeking (224/388, 57.7%), while a small proportion were both informational and emotional help-seeking (44/388, 11.3%), and some (12/388, 3.1%) were emotional help-seeking only. Self-disclosure was identified as a fourth category, comprising almost a quarter of all posts (90/388, 23.2%). A total of 6022 comments were posted in response to the original posts, and there were 4452 reactions across all posts. Emotional help-seeking attracted the most comments per thread (mean 21.5, SD 19.8), followed by informational and emotional help-seeking (mean 20.2, SD 14.7), informational help-seeking (mean 15.6, SD 14.6), and self-disclosure (mean 14.3, SD 21.8). Across all help-seeking categories, few reactions occurred compared to comments; in contrast, self-disclosure attracted a large number of reactions (mean 9.4, SD 45.3).

Conclusions
This is one of the first studies to examine peer support in a GDM online community on Facebook. Our findings suggest that active participants’ needs around information and support in relation to GDM are being somewhat met by peer-led online communities. Given the practical limitations of formal health care, including the provision of ongoing social support, it is important to recognize how GDM online communities can complement formal health care and help address unmet needs.