bluuebunny/crossref_metadata_2025_split huggingface dataset:
New non-radial models for horizontal and acquisition merger to achieve strong efficiency and MPSS without changing in the efficient frontier
Authors: Fakharzadeh A.Reza , Rostamzadeh Hassan 
Published: May 2024

Abstract

In this study, new models for the merger of two or more inefficient units are presented. The discussed mergers in this study are horizontal and acquisition mergers. In presented models, unlike the previously presented models, the mergers are done non-radially; indeed, managers can manage each of the indicators separately in the presented new merger process. In this regard, the efficient frontier of the production possibilities set does not change, and the merger of several units does not affect the efficiency score of other decision-making units. Also, by merging via the new models one can obtain a strong efficient unit with the most productive scale size. The presented models are applied to the horizontal and acquisition merger of Iranian banks.
Computational-Aided Design of Next-Generation Extractants for Trivalent Actinide and Lanthanide Separation
Authors: Xiao Chengliang , Yang Xiaofan , Fang Dong , Chen Linjia , Liu Yaoyang , Wang Shihui , Xu Lei , Zhang Anyun , Su Jing , Xu Chao 
Published: May 2024

Abstract

The chemical similarities between trivalent actinides (An(III)) and lanthanides (Ln(III)) stemming from their analogous ionic radii and physicochemical properties present a significant challenge in differentiating and separating them via solvent extraction, which is a pressing issue in nuclear and radiochemistry. However, the existing approaches suffer from different drawbacks such as inadequate separation factors, limited stripping efficiency, and undesired co-extraction. In this study, a novel unsymmetrical phenanthroline-derived amide-triazine (Et-Tol-CyMe4-ATPhen) extractant was designed with the aid of theoretical calculations and synthesized using an unprecedented de novo construction method. As expected, the Et-Tol-CyMe4-ATPhen extractant exhibited a favorable extraction ability for Am(III) and minimal extraction for Ln(III), thereby achieving an extremely selective An(III)/Ln(III) separation with a separation factor over 280. Furthermore, Am(III) could be effectively stripped from loaded phases using dilute nitric acid. The underlying coordination mechanisms were thoroughly elucidated by using 1H NMR, ESI-MS, UV-Vis absorption spectrum, fluorescence excitation spectrum, and X-ray single-crystal diffraction. This work holds promise for addressing the current challenges in An(III)/Ln(III) separation and represents a pioneering endeavor in developing next-generation extractants from first principles calculation.
Effectiveness and safety of the hyaluronic acid INNORYOS 2.2% in patients with early to advanced knee osteoarthritis. A prospective non-interventional randomized clinical trial.
Authors: Hofweber Lola , von Stengel Simon , Nürnberger Jörg , Kemmler Wolfgang 
Published: May 2024

Abstract

Introduction: We determined the effectiveness and safety of INNORYOS 2.2% hyaluronic acid (HA) containing 15mg of niacinamide on knee pain, stiffness and function in people with knee OA compared with an established agent. Further study objectives were to monitor side effects during the study period.
Methods: The 26-week prospective non-interventional open clinical trial tested on non-inferiority of INNORYOS 2.2% compared to an established HA product (Synvisc 0.8%, 2.0 ml). Briefly, 90 people with knee OA (Kellgren-Lawrence grade I to III), 40–85 years old, were allocated randomly to the two groups. HA injections were carried out at baseline and after 1 and 2 weeks. ANCOVA that adjusted for baseline differences was applied to determine between group differences (i.e. “effects”) after 26 weeks.
Results: None of the 90 participants (65.0 ± 10.5 years, BMI: 30.2 ± 5.5 kg/m2) quit the study or was lost to follow-up. In summary WOMAC pain score (p = .26) and function score (p = .49) did not differ significantly between the groups, but data significant better effect after INNORYOS 2.2% were observed for WOMAC stiffness subscale (p = .009). In detail, changes from baseline to 26-week FU average 43.6% (29.1%) for “pain”, 25.9% (0%) for “stiffness” and 37% (27.6%) for “function” subscale in the INNORYOS (Synvisc) group.
No adverse effects were observed or reported by the participants. As determined by personal interviews and recorded in the questionnaires no changes of co-medication with impact on the study outcomes were observed.
Conclusion: We did not determine significantly lower effects compared with the established product. In contrast, INNORYOS 2.2% tendentially revealed more favourable effect on all WOMAC categories (significant for “stiffness”). Thus, the present study provided further evidence for the effectiveness and safety of INNORYOS 2.2% in patients with early to advanced knee osteoarthritis.
Designing an effective educational toy: incorporating design-thinking and user-centered design in the design classroom
Authors: Houston Natalie , Manrique Ferney 
Published: May 2024

Abstract

“Designing an effective educational toy” is a research project that emerged from a teaching and learning course designed to facilitate creative engagement both inside and outside the classroom at Wenzhou-Kean University in China. This research study examines the incorporation of design thinking and user-centred design concepts in the development of an educational toy by graphic design students. Utilising the ‘Double Diamond Model of Design’, the research methodically progresses through the stages of observation, generation, prototyping, and testing to create a 3D letter-building kit with the goal of improving children's educational experiences. The approach prioritises the integration of ideas through iterative design, taking safety into account, involving users, and focusing on instructional value. The key findings emphasise the effective implementation of design principles in developing a product that not only fulfils safety and educational requirements but also provides novel learning experiences. The essay continues by recommending the wider implementation of these concepts in the creation of educational toys to encourage improved learning outcomes and the development of skills in children.
Harnessing Machine Learning and Ensemble Models for Tourism Potential Zone Prediction for the Assam State of India
Authors: Raha Shrinwantu , Gayen Shasanka Kumar , Deb Sayan 
Published: May 2024

Abstract

Although Assam is enriched with several popular tourist destinations but till date, its’ complete charm remains enigmatic. This research was aimed at prognosticating the Tourism Potential Zone (TPZ) for the state of Assam using five machine learning (i.e., Conditional Inference Tree, Bagged CART, Random Forest, Random Forest with Conditional Inference Tree, and Gradient Boosting models) and one ensemble model. A 5-step methodology was implemented to do this research. First, a Tourism Inventory Database was prepared using the Google earth Imagery, and a rapid field investigation carried out with the help of Global Positioning System and non-participant observation technique. Total 365 tourism points was in the inventory, 70% (224) of which was used for the training set and 30% (124) was used for the validation purpose. The tourism conditioning factors such as Relief, Aspect, Viewshed, Forest Area, Wetland, Coefficient of Variation of rainfall, Reserve Forest, Population Density, Population Growth Rate, Literacy Rate and Road-railway density were used as the independent variables in the modelling process. The TPZ was predicted with the help of above machine learning models and finally, a new TPZ Ensemble Model was proposed by combining each model. The result showed that all machine learning models performed well according to prediction accuracy and finally, the ensemble model outperformed other models by achieving the highest AUC (97.6%), Kappa (0.82) and accuracy (0.93) values. The results obtained from this research using machine learning and ensemble methods can provide proper and significant information for decision makers for the development of tourism in the region.
Implementation of Solar Particle Event Nowcast Approach for Real Time Application
Authors: Burov Vyacheslav , Ochelkov Yuri , Kondratov Andrey , Kholodkov Kirill 
Published: May 2024

Abstract

This short report describes the method of forecasting solar proton events (SPEs) in real time. The implementation monitors electromagnetic flux (particularly, X-ray flux) to pinpoint phases of SPE evolution and possibly predict the oncoming particles tens of minutes beforehand. This approach utilizes statistical methods to provide an algorithm that describes X-ray change vs. time dependency. The implementation is mounted for evaluation in the Russian Segment of China-Russia Consortium - a Global Space Weather Center. As solar maximum is approaching we expect the method to show it's potention in this cycle.
A Comparative Study of Mathematical Models for Two-Dimensional Cutting Stock Problems and Solving Algorithms
Authors: Almani Fatemeh Maleki , Gholamnazhad Mahsa 
Published: May 2024

Abstract

Two-dimensional cutting problem involves cutting a set of small pieces from large sheets in a way that optimizes one or more objectives. The objective of this problem can be minimizing the number of sheets used, waste, cost, and so forth. These problems have various applications in different industries such as glass, paper, wood, plastic, steel, etc. This paper studies one of the recent exact polynomial models proposed for these problems. Although the presented model, named Original(a), is quite promising for finding a solution, since these problems belong to the mixed integer non-linear programming model (MINLP) group, solving them will be difficult. Therefore, a linearization technique by multiplying continuous variables for this model would be used. The goal is to find an optimum solution. The studied models are implemented in the Matlab environment and the results of numerical experiments are presented.
Influence of Interparental Conflict on Teen Dating Violence: A Review
Authors: Kumari Shanta , Gupta Smita 
Published: May 2024

Abstract

Research indicates a significant impact of parenting on adolescents’ (teens’) psychological health. Furthermore, the effect of interparental conflict on adolescents’ internalizing and externalizing behavior has been well researched. However, the effect of interparental conflict (in intact families) on teen dating violence is underresearched. Using a literature review, the present study is an attempt to examine the emerging trends in dating relationships among adolescents. With the help of existing studies, the present paper aims to analyze the association between interparental conflict and dating relationships among teens. This systematic study revealed significant findings. It was found that those teens who perceived interparental conflicts at home, showed maladjustment behavior in their dating relationships. Even the study proposes the relevance of certain interventions that can help teenagers improvise their romantic/partner relationships as well as their parents.
Enhanced biodegradation of phenol under Cr(VI) stress by microbial collaboration and potential application of machine learning for phenol biodegradation
Authors: Bing Wenrong , Li Xinyu , Liang Mingzhao , Zhou Xu , Zhang Jianfeng , Liang Jing 
Published: May 2024

ABSTRACT

Cr(VI) and phenol commonly coexist in wastewater, posing a great threat to the environment and human health. However, it is still a challenge for microorganisms to degrade phenol under high Cr(VI) stress. In this study, the phenol-degrading strain Bacillus cereus ZWB3 was co-cultured with the Cr(VI)-reducing strain Bacillus licheniformis MZ-1 to enhance phenol biodegradation under Cr(Ⅵ) stress. Compared with phenol-degrading strain ZWB3, which has weak tolerance to Cr(Ⅵ), and Cr(Ⅵ)-reducing strain MZ-1, which has no phenol-degrading ability, the co-culture of two strains could significantly increase the degraded rate and capacity of phenol. In addition, the co-cultured strains exhibited phenol degradation ability over a wide pH range (7–10). The reduced content of intracellular proteins and polysaccharides produced by the co-cultured strains contributed to the enhancement of phenol degradation and Cr(Ⅵ) tolerance. The determination coefficients R2, RMSE, and MAPE showed that the BP-ANN model could predict the degradation of phenol under various conditions, which saved time and economic cost. The metabolic pathway of microbial degradation of phenol was deduced by metabolic analysis. This study provides a valuable strategy for wastewater treatment containing Cr(Ⅵ) and phenol.
Synthesis and Antitumor Activities of Novel 5-amino-3-(halophenyl)-1- phenyl-1H-pyrazole-4-carbonitriles
Authors: Abd El-Wahab Ashraf H. F. 
Published: May 2024

:
In this work, a series of novel 3-(halophenyl)-1-phenyl-1H-pyrazole moieties have
been synthesized. Their structures were characterized by IR, NMR, and MS spectroscopy, and
the corresponding antitumor properties were also studied.

Objectives:
This study aimed to synthesize a series of new 3-(halophenyl)-1-phenyl-1Hpyrazole
moieties and survey the antitumor properties of these compounds.

Materials and Methods:
3-(halophenyl)-1-phenyl-1H-pyrazoles (4a-j) were prepared by reaction
of phenyl hydrazine (3) with different halogen aromatic aldehydes 1a-j and malononitrile
(2) in C2H5OH and piperidine. The reaction took place under microwave irradiation settings for
two minutes at140°C.

Results:
Three human cancer cell lines were used as in vitro test subjects for compounds 4a - j.
Three cell lines from mammals HeLa (a cell line for human cervical cancer), MCF-7 (a cell
line for human breast cancer), and PC-3 (a cell line for human prostate cancer), all with 5-
fluorouracil as the standard reference drug were used.

Conclusion:
A series of novel 3-(halophenyl)-1-phenyl-1H-pyrazoles were synthesized in this
work. All substances had their anticancer properties assessed.
Analysis of Seven Terpenoids by HS-SPME Coupled with GC-MS for the
Identification and Classification of Different Teas
Authors: Xie Yangzhou , Yang Yi , Tian Yu , Liu Zhimin , Xu Zhigang , Jiang Wei , Liu Zhihua , Si Xiaoxi 
Published: May 2024

Background:
Terpenoids are essential aroma substances in teas, and their concentration
brings various characteristics to different teas. Therefore, developing a simple and stable method is
necessary for distinguishing tea categories.

Objective:
In previous studies, more attention was paid to non-chiral isomers of terpenes due to the
challenges of separating chiral isomers. So, this paper aims to present a method for effectively separating
seven terpenoid substances, including chiral isomers and non-chiral isomers, to facilitate the
classification and identification of teas.

Methods:
A method utilizing headspace solid-phase microextraction coupled with gas chromatography-
mass spectrometry was used to isolate and analyze 7 terpenoid compounds. After optimized
conditions, the BGB-176 chiral column and the PDMS/DVB fiber were selected for subsequent
analysis.

Results:
This method has a good linear range of 0.1-200 mg/L, and its linear correlation coefficients
are between 0.9974 and 0.9994, and the limit of detection and the limit of quantification is 0.02–0.03
and 0.06–0.09 mg/L, respectively. Only five terpenoid substances were detected in a total of 15 tea
samples. Furthermore, In the detection of carvon and α-ionone optical isomers, the S isomer was
mainly detected.

Conclusions:
An effective approach was developed to separate and analyze 7 terpenoid compounds
in natural and synthetic teas. Meanwhile, 15 tea samples can be identified and classified using principal
component analysis.
Deployment of a Phenotypic Characterization System for Effective Identification of the Onset of Asthma Disease
Authors: R Pooja M. , Ravi Vinayakumar , Al Mazroa Alanoud , Ravi Pradeep 
Published: May 2024

Background
Essentially, machine learning techniques help with clinical decision-making by forecasting prediction results based on recent and historical data, which are frequently found in carefully chosen clinical data repositories. In order to uncover hidden patterns in the data, machine learning applies sophisticated analytical techniques that conduct an exploratory analysis while constructing prediction models to support clinical judgment.

Objective
To effectively identify asthmatics in two distinct cohorts representing India's rural and urban populations by adopting a phenotypic characterization approach.

Methods
Cross-sectional and categorical in design, the data represent the two populations, with clinical history information emphasizing clinical symptoms and patterns defining the condition. The method adopts a hybrid approach since it uniquely blends the unsupervised and supervised learning techniques to explore the advantages of both. The clustering data emphasizing the phenotypic characteristics of asthma is input to the classifier, and the performance of the classifier was continuously monitored for significant improvement in the results.

Results
Asthma disease outcome predictions made by the hybrid decision support system were quite accurate, with classification accuracy reaching up to 85.1% and 95.3% for the two datasets, respectively.

Conclusion
Since asthma is a heterogeneous disease with multiple subtypes, employing clustering information in the form of cluster evaluation scores as an input parameter to the classifiers can effectively predict disease outcomes.
Evaluating the Health Effects of COVID-19 Vaccination on the Population of East Punjab (India): A Comprehensive Survey
Authors: Aggrawal Chirag , Mohan Chander , Ayush  , Gupta Ashna , Kumar Deepak , Kumar Ashish , Kamboj Sweta , Kamboj Shikha , Guarve Kumar , Kamboj Rohit 
Published: May 2024

Background:
The impressive and innovative efforts of scientists worldwide have played a
pivotal role in shaping the development and production of COVID-19 vaccines, widely acknowledged
as vital resources for pandemic control.

Objective:
The primary aim of this investigation was to identify the prevailing effects linked to these
vaccines, particularly considering their implications for human well-being.

Methods:
An online survey was conducted in various cities in India, and a trial version of Qualtrics
CoreXM software was implemented to prototype 22 questionnaires.

Result:
After vaccination, respondents reported health changes: skin (26.9%), eye (22.3%), stomach
issues (22.8%), respiratory (12.5%), and mental health (2.9%). Among 1,047 participants, 8.9% had
intercourse, 11.3% men, 7.3% women. 17.9% were unsure; 19.7% were men, 13.7% were women,
and 37.5% were "others". A majority (73.3%) reported no sexual experiences: 68.9% men, 78.8%
women, and 62.5% "others".

Conclusion:
This study offers an extensive perspective on the vaccination situation in the northern
region of India. It delves into the intricate demographic factors influencing vaccine acceptance and
understanding potential side effects. These discoveries contribute substantially to the ongoing conversation
regarding the COVID-19 vaccination and its various impacts on public health and societal
welfare.
Resistance of Green Treated Dendrocalamus asper (Buluh Betong) against Aspergillus brasiliensis Attack
Authors: Abdul Karim Siti Rafedah , Abdul Kadir Yanti 
Published: May 2024

Dendrocalamus asper, D.asper (Buluh Betong) were pressure treated with a plant-based extract from (Cinnamomum camphor (l.) presl) and Cymbopogon citratus Stapf.). The durability of treated D. asper toward mould (Aspergillus brasiliensis) attack was evaluated. Camphor leaf extract has strong antifungal compounds, including D-camphor, eucalyptol, α-terpineol, linalool, and 4-terpineol that suppress mould activity. Fixing the camphor extract with 10%, MUF increased mould resistance as compared to treatment with camphor extract alone. Treatments were selected based on the initial screening test that showed the inhibitory effect of mould. Results showed the potential of plant extracts as green bamboo treatment.
Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis
Authors: Lau Nancy , Zhao Xin , O'Daffer Alison , Weissman Hannah , Barton Krysta 
Published: May 2024

Background
During the COVID-19 pandemic, Twitter (recently rebranded as “X”) was the most widely used social media platform with over 2 million cancer-related tweets. The increasing use of social media among patients and family members, providers, and organizations has allowed for novel methods of studying cancer communication.

Objective
This study aimed to examine pediatric cancer–related tweets to capture the experiences of patients and survivors of cancer, their caregivers, medical providers, and other stakeholders. We assessed the public sentiment and content of tweets related to pediatric cancer over a time period representative of the COVID-19 pandemic.

Methods
All English-language tweets related to pediatric cancer posted from December 11, 2019, to May 7, 2022, globally, were obtained using the Twitter application programming interface. Sentiment analyses were computed based on Bing, AFINN, and NRC lexicons. We conducted a supplemental nonlexicon-based sentiment analysis with ChatGPT (version 3.0) to validate our findings with a random subset of 150 tweets. We conducted a qualitative content analysis to manually code the content of a random subset of 800 tweets.

Results
A total of 161,135 unique tweets related to pediatric cancer were identified. Sentiment analyses showed that there were more positive words than negative words. Via the Bing lexicon, the most common positive words were support, love, amazing, heaven, and happy, and the most common negative words were grief, risk, hard, abuse, and miss. Via the NRC lexicon, most tweets were categorized under sentiment types of positive, trust, and joy. Overall positive sentiment was consistent across lexicons and confirmed with supplemental ChatGPT (version 3.0) analysis. Percent agreement between raters for qualitative coding was 91%, and the top 10 codes were awareness, personal experiences, research, caregiver experiences, patient experiences, policy and the law, treatment, end of life, pharmaceuticals and drugs, and survivorship. Qualitative content analysis showed that Twitter users commonly used the social media platform to promote public awareness of pediatric cancer and to share personal experiences with pediatric cancer from the perspective of patients or survivors and their caregivers. Twitter was frequently used for health knowledge dissemination of research findings and federal policies that support treatment and affordable medical care.

Conclusions
Twitter may serve as an effective means for researchers to examine pediatric cancer communication and public sentiment around the globe. Despite the public mental health crisis during the COVID-19 pandemic, overall sentiments of pediatric cancer–related tweets were positive. Content of pediatric cancer tweets focused on health and treatment information, social support, and raising awareness of pediatric cancer.