bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of pyrite particle size on the denitrification performance of autotrophic or split‐mixotrophic bioreactors supported by pyrite/polycaprolactone
Authors: Guo Xihui , Peng Gang , Tan Lin , Zhang Yan , Wang Jing , Wang Weibo , Zhang Shiyang 
Published: May 2024

AbstractIn this study, a pyrite‐based autotrophic denitrification (PAD) system, a polycaprolactone (PCL)‐supported heterotrophic denitrification (PHD) system, and a pyrite+PCL‐based split‐mixotrophic denitrification (PPMD) system were constructed. The pyrite particle size was controlled in 1–3, 3–5, or 5–8 mm in both the PAD and PPMD systems to investigate the effect of pyrite particle size on the denitrification performance of autotrophic or split‐mixotrophic bioreactors. It was found that the PAD system achieved the best denitrification efficiency with an average removal rate of 98.98% in the treatment of 1‐ to 3‐mm particle size, whereas it was only 19.24% in the treatment of 5‐ to 8‐mm particle size. At different phases of the whole experiment, the nitrate removal rates of both the PHD and PPMD systems remained stable at a high level (>94%). Compared with the PAD or PHD system, the PPMD system reduced the concentrations of sulfate and chemical oxygen demand in the final effluent efficiently. The interconnection network diagram explained the intrinsic metabolic pathways of nitrogen, sulfur, and carbon in the three denitrification systems at different phases. In addition, the microbial community analysis showed that the PPMD system was beneficial for the enrichment of Firmicutes. Finally, the impact mechanism of pyrite particle size on the performance of the PPMD system was proposed.Practitioner Points
The reduction of pyrite particle size was beneficial for improving the efficiency of the PAD process.
The change in particle size had an effect on NO2−‐N accumulation in the PAD system.
The accumulation of NH4+‐N in the PPMD system increased with the decrease in particle size.
The reduction of pyrite particle size increased the production of SO42− in the PAD and PPMD systems.
The correlations among the effluent indicators of the PAD and PPMD systems could be well explained.
Validity and Reliability of the Swedish Version of the Gugging Swallowing Screen for use in Acute Stroke Care
Authors: Selg Jenny , Holmlund Thorbjörn , Jäghagen Eva Levring , McGreevy Jenny , Svanberg Sara , Wester Per , Hägglund Patricia 
Published: May 2024

Abstract
The purpose of this study was to cross-culturally validate the Swedish version of the Gugging Swallowing Screen (GUSS-S) for use in the acute phase of stroke. Further, to evaluate the inter-rater reliability between different healthcare professionals. GUSS was translated into Swedish using a forward-backward method followed by expert rating to obtain content validity. For criterion validity, the GUSS-S score was compared with Flexible Endoscopic Evaluation of Swallowing (FEES) assessed with the Penetration-Aspiration Scale (PAS) in acute stroke patients (≤ 96 h after stroke onset). Convergent validity was calculated by comparison with the Functional Oral Intake Scale (FOIS) as per the comprehensive FEES assessment, the Standardized Swallowing Assessment (SSA), and the National Institutes of Health Stroke Scale (NIHSS). To evaluate inter-rater reliability, a nurse and a speech-language pathologist (SLP) independently assessed 30 patients. In total, 80 patients (32 women, median age 77 years (range 29–93) were included, mean 1.7 ± 0.9 days after admission. With a cut-off value of 14 points, the GUSS-S identified aspiration with a sensitivity of 100% and a specificity of 73% (area under the curve: 0.87, 95% CI 0.78–0.95). Spearman rank correlation showed very strong correlation between the GUSS-S and PAS (rs=-0.718, P = < 0.001) and FOIS (rs=0.720, P = 0.001) and strong correlation between the GUSS-S and SSA (rs=0.545, P = < 0.001) and NIHSS (rs=-0.447, P = 0.001). The inter-rater agreement for GUSS-S was substantial (Kw=0.67, P = < 0.001). The results indicate that the GUSS-S is a valid and reliable tool for the assessment of dysphagia in acute stroke patients by different healthcare professionals.
Development of a novel laboratory photodynamic therapy device: automated multi-mode LED system for optimum well-plate irradiation
Authors: Yildiz Mustafa Zahid , Kamanli Ali Furkan , Eskiler Gamze Güney , Tabakoğlu Haşim Özgür , Pala Muhammed Ali , Özdemir Ayla Eren 
Published: May 2024

Abstract
Photodynamic therapy (PDT) is a targeted treatment method that utilizes a photosensitizer (PS) to induce cytotoxicity in malignant and non-malignant tumors. Optimization of PDT requires investigation of the selectivity of PS for the target tissues, irradiating light source, irradiation wavelengths, fluence rate, fluence, illumination mode, and overall treatment plan. In this study, we developed the Multi-mode Automatized Well-plate PDT LED Laboratory Irradiation System (MAWPLIS), an innovative device that automates time-consuming well plate light dosage/PS dose measurement experiment. The careful control of LED current and temperature stabilization in the LED module allowed the system to achieve high optical output stability. The MAWPLIS was designed by integrating a 3-axis moving system and motion controller, a quick-switching LED controller unit equipped with interchangeable LED modules capable of employing multiple wavelengths, and a TEC system. The proposed system achieved high optical output stability (1 mW) within the range of 0-500 mW, high wavelength stability (5 nm) at 635 nm, and high temperature stability (0.2 °C) across all radiation modes. The system’s validation involved in vitro analysis using 5-ALA across varying concentrations, incubation periods, light exposures, and wavelengths in HT-29 colon cancer and WI-38 human lung fibroblast cell lines. Specifically, a combination of 405 nm and 635 nm wavelengths was selected to demonstrate enhanced strategies for colon cancer cell eradication and system validation. The MAWPLIS system represents a significant advancement in photodynamic therapy (PDT) research, offering automation and standardization of time-intensive experiments, high stability and precision, and improved PDT efficacy through dual-wavelength integration.
Exploring the impact of SEO-based ranking factors for voice queries through machine learning
Authors: Saeed Zafar , Aslam Fozia , Ghafoor Adnan , Umair Muhammad , Razzak Imran 
Published: May 2024

AbstractThe use of voice search is proliferating and expected to grow into the foreseeable future; this is why websites increasingly optimize their content associated with voice-based search to improve their ranking. In this era of rapid growth in voice search technology, it is a topical matter that needs research. Moreover, many predictions about its future excite the subject and require systematic investigation. This research aims to analyze important features that contribute to the SEO of webpages. Therefore, there is a need to examine various ranking factors that improve the ranking of the webpages for voice search queries on the Search Engine Results Page (SERP). This study consists of two phases. The first phase comprises systematic data acquisition and identifying important SEO-based ranking factors. The second phase includes a longitudinal case study to evaluate the impact and significance of identified factors. To achieve this goal, we conduct experiments on methodical combinations of features through machine learning algorithms such as Support Vector Machine, Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbors, Decision Trees and Random Forest. Comparing results for multiple feature designs evaluates the contributing nature of specific features in SEO-based optimization for ranking. Results suggest the importance of the newly identified feature set (FF) outperforms baselines (EF and EFN) by a significant margin. A longitudinal case study on a blog over four months confirms that optimizing these features improves page ranking; therefore, webmasters must optimize these features while preparing the webpage.
Habitat context and functional growth traits explain alien plant invader impacts on native vegetation communities
Authors: Zeil-Rolfe Isabel , O’Loughlin Luke , Gooden Ben 
Published: May 2024

AbstractExamining context-dependency of ecological impacts of alien plant invasions is critical to further understand the mechanisms driving impacts. We examined how different regional and local habitat contexts influence the abundance-impact associations of an invasive ground-creeper, Tradescantia fluminensis, in native forests across eastern New South Wales (NSW), Australia. Invader impacts were assessed using surveys of resident vegetation at 97 monitoring plots (5 m × 2 m) located at 14 sites, representing a gradient of T. fluminensis abundance. We modelled the association of T. fluminensis invasion with native species richness (number per plot) and foliage cover across two different habitat types (remnant vs replanted forests), two vegetation community types (wet sclerophyll vs river oak forests), and two regions (northern vs. southern NSW). We also modelled variation in native species responses amongst different functional growth forms. The negative associations of T. fluminensis and native species communities was more strongly explained by local site variables (i.e., habitat type, community type, plant growth form) than regional scales. Native richness reduced with invasion in river oak but not wet sclerophyll forest. Native richness also declined in remnant forest, although no effect of invasion was observed in replanted forest. Surprisingly, native species growth forms most like T. fluminensis (ground layer herbs, ferns) were more resistant to reductions in native richness compared to divergent forms (shrub, tree and woody vine recruits). This study highlights the need to explicitly consider local community and habitat context and functional representation of resident species when considering invader impacts and site-level management plans.
Simulating climate policies influences how laypersons evaluate the effectiveness of climate protection measures
Authors: Landmann Helen , Ziegler Wiebke , Gaschler Robert 
Published: May 2024

AbstractClimate change simulations allow the experience of complex processes in rapid progression. Additionally, they hold the potential to enable citizens to quickly evaluate the impact of measures offered as political options to mitigate climate change. Taking En-roads as a test case, we investigated whether exposure to a web-based climate simulation influences laypersons’ views on effectiveness of such measures with an experiment in Germany (N = 271). High usability ratings ascertained that the simulation can be used by lay-persons without detailed support. In line with this, app usage was effective. Using the climate simulation led to higher self-efficacy with regard to being able to evaluate policies with the help of tools. Moreover, comparisons with the control group suggested that app usage affects beliefs about the impact of specific measures such as CO2 pricing. Taken together, the results suggest that online climate simulations such as En-roads can help inform and empower citizens in the process of mitigation of and adaptation to climate change.
Correlation of protein binding pocket properties with hits’ chemistries used in generation of ultra-large virtual libraries
Authors: Song Robert X. , Nicklaus Marc C. , Tarasova Nadya I. 
Published: May 2024

AbstractAlthough the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki–Miyaura, Hiyama and Liebeskind–Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.
Implantation of a novel insertable cardiac monitor: preliminary multicenter experience in Europe
Authors: Fareh S. , Nardi S. , Argenziano L. , Diamante A. , Scala F. , Mandurino C. , Magnocavallo M. , Poggio L. , Scarano M. , Gianfrancesco D. , Palma F. , Silvetti M. S. , Porcelli D. , Racheli M. , Montoy M. , Charles P. , Campari M. , Valsecchi S. , Lavalle C. 
Published: May 2024

Abstract
Background
The LUX-Dx™ is a novel insertable cardiac monitor (ICM) introduced into the European market since October 2022.

Purpose
The aim of this investigation was to provide a comprehensive description of the ICM implantation experience in Europe during its initial year of commercial use.

Methods
The system comprises an incision tool and a single-piece insertion tool pre-loaded with the small ICM. The implantation procedure involves incision, creation of a device pocket, insertion of the ICM, verification of sensing, and incision closure. Patients receive a mobile device with a preloaded App, connecting to their ICM and transmitting data to the management system. Data collected at European centers were analyzed at the time of implantation and before patient discharge.

Results
A total of 368 implantation procedures were conducted across 23 centers. Syncope (235, 64%) and cryptogenic stroke (34, 9%) were the most frequent indications for ICM. Most procedures (338, 92%) were performed in electrophysiology laboratories. All ICMs were successfully implanted in the left parasternal region, oriented at 45° in 323 (88%) patients. Repositioning was necessary after sensing verification in 9 (2%) patients. No procedural complications were reported, with a median time from skin incision to suture of 4 min (25th–75th percentiles 2–7). At implantation, the mean R-wave amplitude was 0.39 ± 0.30 mV and the P-wave visibility was 91 ± 20%. Sensing parameters remained stable until pre-discharge and were not influenced by patient characteristics or indications. Procedural times were fast, exhibited consistency across patient groups, and improved after an initial experience with the system. Operator Operator feedback on the system was positive. Patients reported very good ease of use of the App and low levels of discomfort after implantation.

Conclusions
LUX-Dx™ implantation appears efficient and straightforward, with favorable post-implantation sensing values and associated with positive feedback from operators and patients.

Graphical Abstract
Effectiveness of a Behavioral Activation Intervention for Peripartum Women with Opioid Use Disorder
Authors: Vilensky Michael R. , Arrato Nicole A. , Carpenter Kristen M. 
Published: May 2024

AbstractPregnant women with opioid use disorder show elevated rates of comorbid mental health problems, both of which are associated with negative health outcomes for mothers and children. There is substantial evidence supporting the benefits of treatment of perinatal opioid use disorder, as well as perinatal depression and anxiety, but there are gaps in knowledge about the effectiveness of perinatal behavioral health interventions in the context of co-occurring substance use disorder. The current study seeks to address this gap by examining outcomes of a behavioral activation treatment in a group of peripartum women with opioid use disorder (N = 68). Behavioral activation has shown promise in treating co-occurring depression and substance use problems. The intervention was delivered as part of an integrated care treatment model, in which patients received co-located obstetric, substance use, and mental health care in a hospital-based clinic. Hierarchical linear modeling was used to assess change in symptoms over time. Results suggest that the group behavioral activation intervention was associated with reduced depression and anxiety symptoms, demonstrated by significant reductions in PHQ-9 and GAD-7 scores over the course of treatment. Moreover, there were indications that increased attendance was associated with further reductions in depressive symptoms. Results contribute to understanding the effectiveness of behavioral activation in the context of peripartum opioid use disorder. Findings also add to the evidence supporting integrated care models and offer a potential blueprint for improving outcomes and reducing barriers to care in this population.
Random Matching in 2D with Exponent 2 for Gaussian Densities
Authors: Caglioti Emanuele , Pieroni Francesca 
Published: May 2024

AbstractWe solve the Random Euclidean Matching problem with exponent 2 for the Gaussian distribution defined on the plane. Previous works by Ledoux and Talagrand determined the leading behavior of the average cost up to a multiplicative constant. We explicitly determine the constant, showing that the average cost is proportional to $$(\log \, N)^2,$$

(
log

N
)

2

,

where N is the number of points. Our approach relies on a geometric decomposition allowing an explicit computation of the constant. Our results illustrate the potential for exact solutions of random matching problems for many distributions defined on unbounded domains on the plane.
Madam Yellen is right about minimum wage policies: evidence from millions of sole proprietors
Authors: Gao Xiaohui 
Published: May 2024

AbstractDuring her nomination hearing, Janet Yellen defended the benefits of a higher minimum wage (Question 66 and follow-up). This study analyzes the impact of minimum wage policies on the profitability of sole proprietors by using a dataset of 430 million reported net incomes with the IRS from 2004 to 2018. Three methods are used to examine the relationship between minimum wage increases and net income for sole proprietors: A panel regression framework that includes all counties, comparing contiguous border county-pairs, and a difference-in-differences approach. We find that minimum wage hikes are associated with a higher net income for sole proprietors, with estimated elasticity between 0.15 and 0.2.
Toward sustainable process industry based on knowledge graph: a case study of papermaking process fault diagnosis
Authors: Liang Xiangyao , Zhang Qingyuan , Man Yi , He Zhenglei 
Published: May 2024

AbstractProcess industry suffers from production management in terms of efficiency promotion and waste reduction in large scale manufacturing due to poor organization of the intricate relational databases. In order to enhance the suitability of intelligent manufacturing systems in process industry, this study proposed an innovative top-down structure Knowledge Graph (KG) for process fault diagnosis, and papermaking was taken as a case study. The KG consists of a normalized seven-step-built ontology, which extracted instances of papermaking knowledge via Protégé software. The exported OWL file was imported into Neo4j software for visualization of the KG. The application in papermaking drying process for fault diagnosis shows that it can depict the material and energy flows throughout the process with a clearer relationship visualization than traditional measures. They also enable rationale search for faults and identification of their potential causes. The built KG efficiently manages the vast knowledge of the process, stores unstructured data, and promotes the intelligent development of process with high reusability and dynamicity that can rapidly import new production knowledge as well as flexibly self-updating.
Biological activity, chemical composition, and molecular docking of Eugenia punicifolia (Kunth) DC
Authors: Dogara Abdulrahman Mahmoud , Ibrahim Muhammad Tukur , Mahmud Aisha Abdullahi , Danladi Musa Daniel , Lema Abubakar Abdullahi , Usman Muhammad , Tahir Abdulsalam Salisu , Tabti Kamal 
Published: May 2024

AbstractThere is a significant effort to explore the potential pharmacological activity of natural products derived from plant extracts. The goal is to generate new compounds that could be used for therapeutic purposes. Eugenia punicifolia is a shrub that grows widely all over the world. As a natural cure for diabetes, fever, flu and inflammation, leaves of this medicinal plant are often used in decoction or aqueous infusion. The goal of the study was to record the molecular docking, antioxidant, chemical composition, and alpha-glucosidase inhibition of E. punicifolia. A number of parameters were evaluated in order to confirm its efficacy, including Total Phenolic Content (TPC), TFC (Total Flavonoid Content), FRAP (ferric reducing antioxidant power), DPPH radical scavenging activity, inhibition of α-glucosidase, chemical composition, and molecular docking. The leaf ethanolic exhibited robust antioxidant activity with respect to radical scavenging activity, TPC (Total phenolic content), reducing power, TFC (Total Flavonoid Content), and alpha-glucosidase inhibitory activity at concentrations of 26.7 µg/mL, 18.9 mmol/g, 11.9 mg GAE/g, 13.5 mg QE/g, and 18.9 µg/mL, respectively. In the ethanolic leaf extract, Z-8-Methyl-9-tetradecenoic acid (28.6%) was the main component. The five compounds (− 121.208 kcal/mol, − 120.465 kcal/mol, − 105.572 kcal/mol, − 104.497 kcal/mol, and − 102.256 kcal/mol) were selected as the top lead compounds, respectively. The lead compounds that were found had drug-like properties and were suitable for oral administration. In addition, it was noted that they exhibited non-toxic features and favorable ADME-Tox characteristics, except for compound 6, which displayed toxicity. The leaves exhibited potential applications in the food and pharmaceutical sectors.
Innovative application of artificial intelligence in a multi-dimensional communication research analysis: a critical review
Authors: Asif Muhammad , Gouqing Zhou 
Published: May 2024

AbstractArtificial intelligence (AI) imitates the human brain’s capacity for problem-solving and making decisions by using computers and other devices. People engage with artificial intelligence-enabled products like virtual agents, social bots, and language-generation software, to name a few. The paradigms of communication theory, which have historically put a significant focus on human-to-human communication, do not easily match these gadgets. AI in multidimensional touch is the subject of this review article, which provides a comprehensive analysis of the most recent research published in the field of AI, specifically related to communication. Additionally, we considered several theories and models (communication theory, AI-based persuasion theory, social exchange theory, Frames of mind, Neural network model, L-LDA model, and Routine model) to explain a complex phenomenon and to create a conceptual framework that is appropriate for this goal and a voluntary relationship between two or more people that lasts for an extended period. Communication and media studies focus on human–machine communication (HMC), a rapidly developing research area. It is our intention to continue investigating the beneficial and detrimental effects of artificial intelligence on human communication as well as to identify novel concepts, theories, and challenges as the research process develops.