bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ab initio Valence Bond Theory with Density Functional
Authors: Zhou Chen , Wu Xun , Zheng Peikun , Ying Fuming , Su Peifeng , Wu Wei 
Published: November 2022

Abstract:
The accurate description of strongly correlated systems, also known as multireference
systems, requires a balanced treatment of static and dynamic correlations and is an important target
for developing quantum chemical methods. An appealing treatment to economically describe
strongly correlated systems is the multireference density function theory (MRDFT) approach, in
which the static correlation is included in the multiconfigurational wave function, while the density
functional includes the dynamic correlation. This mini-review focuses on the recent progress and
applications of the density functional methods based on valence bond theory. A series of density
functional valence bond (DFVB) methods are surveyed, including the dynamic correlation correction-
based and Hamiltonian matrix correction-based DFVB methods, the hybrid one-parameter
DFVB methods, the block-localized density functional theory and the multistate density functional
theory. These methods have been applied to various chemical and physical property calculations of
strongly correlated systems, including resonance energies, potential energy curves, spectroscopic
constants, atomization energies, spin state energy gaps, excitation energies, and reaction barriers.
Most of the test results show that the density functional methods based on VB theory give comparable
accuracy but require lower computational cost than high-level quantum computational methods
and thus provide a promising strategy for studying strongly correlated systems.
Morelloflavone as Phytomedicine-An Ethnopharmacological Review on the Therapeutical Properties, Biological Efficacy and Pharmacological Activity
Authors: Patel Dinesh Kumar 
Published: November 2022

Background:Flavonoids are low molecular weight class secondary metabolites found to be present throughout the plant kingdom. Structurally, flavonoid class phytochemicals consist of C6-C3-C6 chemical structures with different substitution patterns and have drug-like nature. Morelloflavone is a biflavonoid class phytochemical found to be present abundantly in the Garcinia genus. Morelloflavone has been investigated in the scientific field for its numerous pharmacological activities. The present work aims to discuss the scientific progress of health beneficial aspects and pharmacological potential of morelloflavone in medicine.Methods:The purpose of this work is to provide scientific information about the biological potential and health beneficial effects of flavonoids in medicine with special reference to the morelloflavone. In this work, we have collected the scientific data of morelloflavone for the biological potential and pharmacological activities against different types of human illnesses with their mechanism of action from Google, Google Scholar, Science Direct and PubMed and analyzed. Further, the preventive role of morelloflavone against various types of human illnesses and the mechanism of action have also been discussed in the present work. This work will provide upto- date information on the health beneficial aspects of morelloflavone in medicine for the prevention and treatment of human complications.Results:Morelloflavone exhibited a wide-range of pharmacologic activity in regulating numerous kinds of human disorders. Present work revealed the health beneficial aspects and pharmacological activities of morelloflavone for the prevention and treatment of human disorders due to its anti-microbial, anti-plasmodial, anti-oxidant, anti-atherosclerosis, anti-inflammatory, anti-tumor anti-restenosis, anti-HIV, aphrodisiac, anti-Alzheimer's and apoptotic potential in the medicine. Further, its biological potential on phospholipase A2, melanin, kallikreins, monoamine oxidase, aromatase, Eg5 Inhibition and proprotein convertases have been also analyzed in the present work. Analytical data signified the importance of modern analytical tools for the separation and isolation of morelloflavone in medicine.Conclusion:Present work revealed the detailed pharmacological activities of morelloflavone that can be utilized in the future for the development of new pharmacologic agents.
State-of-the-Art Evidence Retriever for Precision Medicine: Algorithm Development and Validation
Authors: Jin Qiao , Tan Chuanqi , Chen Mosha , Yan Ming , Zhang Ningyu , Huang Songfang , Liu Xiaozhong 
Published: November 2022

BackgroundUnder the paradigm of precision medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available clinical evidence, including results from case reports, clinical trials, and systematic reviews. However, it is increasingly difficult for physicians to find such evidence from scientific publications, whose size is growing at an unprecedented pace.ObjectiveIn this work, we propose the PM-Search system to facilitate the retrieval of clinical literature that contains critical evidence for or against giving specific therapies to certain cancer patients.MethodsThe PM-Search system combines a baseline retriever that selects document candidates at a large scale and an evidence reranker that finely reorders the candidates based on their evidence quality. The baseline retriever uses query expansion and keyword matching with the ElasticSearch retrieval engine, and the evidence reranker fits pretrained language models to expert annotations that are derived from an active learning strategy.ResultsThe PM-Search system achieved the best performance in the retrieval of high-quality clinical evidence at the Text Retrieval Conference PM Track 2020, outperforming the second-ranking systems by large margins (0.4780 vs 0.4238 for standard normalized discounted cumulative gain at rank 30 and 0.4519 vs 0.4193 for exponential normalized discounted cumulative gain at rank 30).ConclusionsWe present PM-Search, a state-of-the-art search engine to assist the practicing of evidence-based PM. PM-Search uses a novel Bidirectional Encoder Representations from Transformers for Biomedical Text Mining–based active learning strategy that models evidence quality and improves the model performance. Our analyses show that evidence quality is a distinct aspect from general relevance, and specific modeling of evidence quality beyond general relevance is required for a PM search engine.
Цифровая трансформация письменного наследия тибетского буддизма: состояние и перспективы
Authors:   Institute for Mongolian, Buddhist and Tibetan Studies of the Siberian Branch of the RAS (6, Sakhyanova St., 670047 Ulan-Ude, Russian Federation), Bazarov Boris V. , Rinchinov Oleg S. , Bazarov Andrei  A. 
Published: November 2022

Introduction. Making public knowledge pertaining to Buddhist culture proves important and topical enough due to the steady growth of global significance attributed to East and Central Asia. Therefore, insights into monuments of Buddhist heritage represented in vast collections of Russia — at advanced academic and technological levels with an emphasis on the active use of digital technologies — shall yield solutions to quite a number of sociocultural development problems, and strengthen positions of our nation in Asia’s geopolitical environments. Goals. The study aims at analyzing the current state of the art in digitization of Tibetan Buddhism’s written heritage and assessing some promising AI related technologies development trends. Materials and methods. The study provides a comparative analysis of activities by the Buddhist Digital Resource Center (BDRC) consortium, an oldest and most successful initiatives operating since 1999, for advanced approaches in this field. The use of artificial intelligence methods to solve the problem of creating a machine-readable text corpus of Buddhist writings and machine translation is considered as a promising direction. Results. The work discloses key principles of digitization and representation of written monuments implemented on the new digital platform BUDA (Buddhist Digital Archives), shows effectiveness of the underlying IIIF (International Image Interoperability Framework) and LOD (Linked Open Data) architectures. The efforts to assess artificial intelligence technologies prospects included a pilot study on the use of ‘deep learning’ neural network methods to create an experimental model for optical recognition of Tibetan characters which yielded a result of 94 % of recognized characters. This lays a foundation for flow-line decoding of Tibetan-language scans and creating a comprehensive corpus of Buddhist writings in a machine-readable format, which offers the challenge of developing new efficient digital textual tools. Conclusions. The integration of existing and promising approaches allows for a digital transformation of Buddhist written traditions, the latter to open new functioning forms and development opportunities in the contemporary world backed with a possibility to reveal the cultural and intellectual potential of Buddhist civilization for modern society to the full.
Customer churn prediction model: a case of the telecommunication market
Authors: Fareniuk Yana , Zatonatska Tetiana , Dluhopolskyi Oleksandr , Kovalenko Oksana 
Published: November 2022

AbstractThe telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results
Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models
Authors: Hosseini Seyed Hassan , Gentile Guido 
Published: November 2022

Abstract
The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model. Several public transport trips with different users were saved in Rome to test our access trip phase recognition algorithm. We also used Convolutional Neural Network as a deep learning algorithm to automatically extract features from one sensor (linear accelerometer), obtaining a model that performs well in predicting five modes of transport with the highest accuracy of 0.81%.
Highway Vehicles’ Overtaking Classification
Authors: Aldulaimi Hamid Shimal , Ben Hamed Bassem 
Published: November 2022

Abstract
Highway users can apprehension to certain subjects with utilizing of Vehicular Ad-hoc Networks (VANET) applications if the rules for safe overtaking movement are violated to make the lane change maneuver between vehicles on the highway road. In our research, we suggest an algorithm for semi-automated vehicles S-AV compliant lane change to emphasize rules for safe overtaking between vehicles on the highway. The proposed algorithm technique classify the safe overtaking into major categories and critically analyzed them depending on various classes of lane change movements between vehicles interrelated to road condition based on different performance criteria; this technique will add awareness to drivers traveling on highway to increasing the comfort and safety of driving. Finally, we have conclude and suggest research issues associated to Vehicular Ad-hoc Networks to investigate and ensure the real-time decision of safe overtaking between vehicles on the highway, which is important research task to motivate researchers to connect the semi-automated vehicles with driver face emotion detection and increase driving safety.
Ec-Funded Projects’ Lessons Learned In Earth Friendly Freight Transportation
Authors: Yatskiv (Jackiva) Irina , Brauner Thomas , Vesjolijs Aleksejs , Petrovs Vladimir 
Published: November 2022

Abstract
The paper is based on the research project ePIcenter (www.ePIcenterproject.eu) supported by the EU HORIZON 2020 Programme. ePIcenter connects thirty-six partners: port authorities, logistic service providers, manufacturers, academic institutions, and technology partners. The main goal is to develop and test AI driven logistic software solutions, apply new technologies and methodologies to increase the efficiency of global supply chains and reduce their environmental impact. One of the significant aspect the project focusses on is optimisation, using AI, digitalisation, automation and innovations in freight transport and handling technologies. Finally, modelling powerful solutions to enable resilient, efficient and environment friendly supply chains.
Knowledge sharing is one of powerful tool for researchers, policymakers, service providers and other stakeholders to develop a holistic and comprehensive common knowledge base. First, a theoretical framework had been developed through identification and review knowledge available from previous projects funded through the European Commission as well as other international funding projects. Second, lessons learned and success stories from previous EC-funded projects and other international research programmes were reviewed and provided in the first year of project implementation.
Cultivating a Therapeutic Compassionate Relationship: The 3S Approach
Authors: Samoutis George ,   Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus,   George Samoutis, Primary Care and Population Health, University of Nicosia Medical School, 21 Ilia Papakyriakou, Engomi, P.O. Box 24005 1700, Nicosia, Cyprus, Samouti Sophronia ,   International Institute for Compassionate Care - Youth Unit, Nicosia, Cyprus, Aristodemou Pansemni A ,   International Institute for Compassionate Care - Academic Commitee, Nicosia Cyprus
Published: November 2022

In the last decade, a plethora of healthcare research and literature was produced and, indeed, confirms the absolute need to cultivate a therapeutic and compassionate relationship between carer and patient/family, especially in the face of a long-term and /or life-threatening condition. We introduce the 3S model as an approach to cultivate a therapeutic relationship between the carer and the patient/family. It is based on some fundamental traditional skills which may be innate for some but may need to be awakened and cultivated for others, all for the benefit of each of the members of the involved triad: patients, family, and healthcare professionals.
The 3S approach aids in developing a therapeutic relationship that involves compassion and can be easily applied with significant results, especially in the context of chronic disease management. However, more research is needed to quantify the impact of this 3S approach on the therapeutic relationship and chronic disease management.
Evaluation of efficacy of Aloe Vera as a Disinfectant by Immersion and Spray methods on Irreversible Hydrocolloid Impression Material and its Effect on the Dimensional Stability of Resultant Gypsum Cast - An in Vitro Study
Authors: Trivedi Roopsi ,   Department of Prosthodontics, Sardar Patel Postgraduate Institute of Dental and Medical Sciences, Lucknow, Uttar Pradesh, India., Sangur Rajashekar ,   Department of Prosthodontics, Rama Dental College Hospital and Research Centre, Kanpur, Uttar Pradesh, India., Rao Bathala Lakshmana ,   Department of Prosthodontics, Lenora Institute of Dental Sciences, Rajahmundry, Andhra Pradesh, India.,   Dr. Lakshmana Rao Bathala, Department of Prosthodontics, Lenora Institute of Dental Sciences, Rajahmundry, Andhra Pradesh, 533103, India, E-mail: kushulubathala@gmail.com, Srivastava Shitij ,   Department of Prosthodontics, Sardar Patel Postgraduate Institute of Dental and Medical Sciences, Lucknow, Uttar Pradesh, India., Madhav Srishti ,   Dental College Azamgarh, Azamgarh, Uttar Pradesh, India., Chaubey Preeti ,   Department of Prosthodontics, Sardar Patel Postgraduate Institute of Dental and Medical Sciences, Lucknow, Uttar Pradesh, India.
Published: November 2022

The need to use a natural alternative for disinfecting dental impression materials, which should be biocompatible and effective, led us to evaluate the efficacy of Aloe vera as a disinfectant by immersion and spray method on alginate impression material and its effect on the dimensional stability of the resultant gypsum cast.The efficacy of the disinfection procedures against Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans was evaluated by determining the number of colony-forming units (CFU) recovered after disinfection of alginate discs inoculated with 1x106 CFU for defined intervals with aloe vera solution (99.96%). Dimensional stability was determined from the mean percentage deviation of three measurements that were taken between the fixed points on the casts using a traveling microscope and compared with corresponding measurements from the master model and controls. Statistical analysis of data was determined by analysis of variance.We found out that there is a mean percentage reduction in colony count of S. aureus, P. aeruginosa, and C. albicans after 3 minutes of immersion in aloe vera and after 3 minutes spray disinfection. Complete elimination of all the microorganism cells after 7 min immersion and spray disinfection. There was a statistically significant difference in the increase of the mean anteroposterior (AP) and cross arch (CA) dimension after 3 and 7 minutes immersion in aloe vera.Spraying with aloe vera for 7 minutes was proved to be the most effective disinfection procedure without altering dimensional stability.
The Role of Social Capital on the Health Promotion Behaviors in Women Teachers at Bam Girls’ Schools
Authors: Asma Pourtaheri ,   School of Public Health, Bam University of Medical Sciences, Bam, Iran, Bafghi Mohammad Javad Akbarian ,   School of Public Health, Bam University of Medical Sciences, Bam, Iran, Behzad Mohammadi ,   School of Public Health, Bam University of Medical Sciences, Bam, Iran, Mohammad Reza Rajabalipour ,   School of Public Health, Bam University of Medical Sciences, Bam, Iran,   Rajabalipour Mohammad Reza, Instructor, Master of Health Education and promotion, School of Public Health, Bam University of Medical Sciences, Bam, Iran, E-mail: mmrr1366@yahoo.com, Phone: 00989139969382
Published: November 2022

A healthy lifestyle prevents more than half of diseases and disorders in humans. Social capital is a decisive factor in lifestyle modification. The present study aimed to investigate the effect of social capital on health promotion behaviors in a group of teachers from Bam, Kerman, Iran.
The present study was a descriptive-analytic cross-sectional study. The participants were 245 secondary school teachers that were studied using census data. The integration of two standard questionnaires in the field of health promotion behaviors and social capital were the research instruments. Data analysis was done using the SPSS v23 software.
The average age of the participants was 39.4 years. Also, none of the demographic variables had a significant effect on health promotion behaviors. The highest and lowest score of practices were related to religion believes with an average of 14.47 and physical activities with an average of 10.64. Finally, the average of health promotion behaviors and social capital in women teachers were evaluated at the desirable level.
Considering the role of teachers in educating and providing knowledge to future generations, it can positively affect the health of the whole society through effective interventions on this group. This requires paying more attention to the empowerment and promote the individual and groups of teachers. Therefore, one of the natural and inexpensive ways to improve society’s health is to promote health components in teachers and their self-sufficiency in the field of health.