bluuebunny/crossref_metadata_2025_split huggingface dataset:
Zero-touch Networks for Intelligent Manufacturing: An End-to-end Serverless-based Learning Approach
Authors: Lin Chia-Hung , Tello-Oquendo Luis , Lee Myungjin , Lien Shao-Yu , Subramaniam Suresh , Matsuura Motoharu , Hasegawa Hiroshi , Lin Shih-Chun 
Published: June 2023

Abstract
Industry 4.0-enabled smart factories are expected to realize the next revolution for manufacturers. Although artificial intelligence (AI) technologies have improved productivity, current use cases belong mainly to small-scale and single-task operations.A zero-touch network platform is developed for intelligent manufacturing, which unbound the potential of a smart factory by facilitating distributed AI applications in both training and inferring stages in a large-scale manner.The open radio access network (O-RAN) architecture is first introduced for the zero-touch network platform to enable globally controlling communications and computation infrastructure capability in the field.Then, a devised serverless-based framework allows intelligent and efficient learning assignments and resource allocations.Hence, requested learning tasks can be assigned to appropriate robots, and the underlying infrastructure can be used to support the learning tasks without expert knowledge.Due to the proposed network system's flexibility, powerful AI-enabled networking algorithms can ensure service-level agreements and superior performance for factory workloads.Finally, three open research directions related to backward compatibility, end-to-end enhancements, and cybersecurity are discussed for a zero-touch smart factory.
Women Empowerment, Parental Education and Future Human Capital Formation (Evidences from Urban Slum Area in India)
Authors: Das Simontini , Chakraborty Bidisha 
Published: June 2023

Abstract
The present paper intends to investigate the impact of women empowerment on future human capital formation in urban slum area of Kolkata, India. Education and health are two important dimensions of human capital formation. We construct women empowerment index comprising of legal and political dimension, interpersonal dimension sociocultural and economic dimension. To estimate the impact of women empowerment on education of the child, Heckman selection model is used while Logit model is used to study the impact of women empowerment on nutrition status of children. The empirical results indicate though women taking decision regarding child schooling has positive significant impact on school enrolment and continuing child’s education, financial independence plays a significant positive role only in continuing education of the child but not enrolling the children to education. We find that women empowerment has positive and significant impact on enrolling their children to school as well as continuing their school education. The positive effect of women empowerment index on the enrollment and school going behavior are stronger for female child than male child. Regarding health issues, we find that the probability of having a well-nourished child increases with the rise in the status of mothers’ empowerment, irrespective of the gender of the child. Other control variables, like monthly per capita income of the household, parental educational level especially mothers’ education level and co-habiting parents have expected significant positive impact on the education status of the children.
JEL Classification: J16, J13, J24, J71
Nutrient distribution in silver birch (Betula pendula Roth) biomass grown in nutrient-poor soil
Authors: Rustowska Beata 
Published: June 2023

Abstract

This study was aimed at evaluating the distribution of nutrients in the organs of silver birch growing on extremely nutrient-poor stands associated with inland dunes in central Poland. The study covered three stands, aged 12, 20, and 34 years. Ten average trees were sampled from each stand, including their fine roots, coarse roots, stemwood, bark, coarse branches, fine branches, and leaves. Under each tree, the soils were sampled at depths of 0–10, 10–20, 20–40, and 40–80 cm. The contents of nitrogen (N), phosphorous (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) were analyzed in the biomass and soil samples. The studied soils were classified as Arenosols; they were strongly acidic and very poor in the studied elements. The nutrient distribution in the birch biomass was highly variable. Most had their highest concentrations in the leaves, followed by the bark (N, S, Cu), roots (P, K, Mg), and branches (Ca). The highest amounts of Fe were in fine roots, whereas Mn and Zn were most abundant in the bark. Wide variability was also apparent in the bioaccumulation factors. These were usually the highest in the leaves or bark and the lowest in the stemwood. Nitrogen showed the highest bioaccumulation intensity among the studied elements, followed by S, Zn, Cu, Mn, and P. The bioaccumulation intensity of the nutrients varied among the stands, in many cases showing significant correlations (both positive and negative) with stand age.
The Impact of Parents' Entrepreneurial Attitudes on Children's Educational Choices in Brazil: A Propensity Score Matching Analysis
Authors: Benevit Bruno , Uhr Daniel , Uhr Júlia Ziero 
Published: June 2023

Abstract
This study employs propensity score matching with survey weight adjustment to identify the effects of parental entrepreneurial attitudes on their children's years of schooling, the likelihood of attending private schools, and the probability of enrolling in university education (public, private, or general). The microdata is from the 2015 National Household Sample Survey. The study incorporates a series of robustness tests and examines heterogeneous treatment effects. The findings indicate that parents with entrepreneurial backgrounds exhibit a greater preference for enrolling their children in private schools. This effect is amplified when both parents are entrepreneurs. The study further reveals that children of entrepreneurial parents are more likely to attend universities, with a stronger preference for private institutions. The analysis of the heterogeneous treatment effect also emphasizes how the gender of the children and the parent's entrepreneurial background significantly influence the magnitude of the treatment.
JEL: J12, J24, L26.
Tokenization of rental real estate assets using blockchain technology
Authors: Rath Ashish , Pateriya R. K. , Tomar Deepak , Singh Surendra 
Published: June 2023

Abstract
The tokenization of rental real estate assets using blockchain technology has recentlygained attention as a novel way to enhance the liquidity and transparency of the real estate market. Inthis paper, we propose a smart contract-based system for the tokenization of rental estate assets usingthe Solidity programming language along with the framework. The system aims to provide a secureand transparent way to buy and sell a fraction of ownership of rental estate assets by leveraging thedecentralized and immutable nature of blockchain technology. Our study showcases the outcomes ofthe regression analysis conducted on synthetic historical data of rental estate returns and risk factors.The findings revealed a significant positive correlation between rental estate returns or rental yieldand risk-return factors. Additionally, we describe the Analysis of Variance(ANOVA) analysis, whichconfirms the statistical significance of our proposed model. Our findings suggest that tokenizationof rental estate assets using blockchain technology can bring substantial benefits to estate investors,such as increased liquidity and reduced transaction costs. This study contributes to the developmentof the estate industry by proposing a novel model of blockchain technology in the context of rentalestate asset tokenization.
Review and proposition of energy communities: The case study of Vitoria-Gasteiz
Authors: Mikel Garro-Aguilar , Ana Picallo-Perez 
Published: June 2023

Abstract
In recent years, the European Union has promoted the concept of collective self-consumption and the so-called energy communities, with the aim of involving citizens in energy management and offering measures to improve energy efficiency. Spain, following the EU directives, promotes a shared use of energy, especially in urban environments. Therefore, and in accordance with this objective, this work presents the case study of incorporating energy communities including public residential buildings in Vitoria-Gasteiz (north of Spain) belonging to the public institution of the Basque Government. The aim of the study is to identify the possibilities of creating energy communities based on PV energy systems, and to analyze their feasibility considering the self-production and consumption. In addition, the possibility of including a private building rooftop of the Mercedes-Benz factory is also studied in order to produce a greater amount of energy and to be able to supply the completely electric demand of the public residential buildings. In the latter case, an economic analysis is carried out, considering the initial investment of a nearly 5 MWp photovoltaic installation, with a 2 years payback, and an environmental analysis, which shows that this photovoltaic installation would mean a saving of almost 1,600 tons of CO2 per year.
Peach Leaf Diseases Identification Using Convolutional Neural Network and Fastai Framework
Authors: Zhang Daping , Yang Hongyu , Cao Jiayu , Gong Qingtao , Kumaran Shamini Raja 
Published: June 2023

Abstract
Peach fruit is an ephemeral tree native to the locality of Northwest China between Tarim Basin and Kunlun Mountains' north, where it was first cultivated. Peaches have quite several health benefits such as improvement in digestion, smoother skin, and relief from allergies. However, the growing quality of peaches is a challenging task due to the common peach diseases such as brown rot, bacterial canker, peach scab, and many more. Traditionally, farmers identify peach diseases by naked eyes from the peach leaves, which is often inaccurate and unstable due to the lack of professional knowledge. Therefore, this research developed an integrated algorithm using a convolutional neural network (CNN) and Fastai Framework, known as CNN-F for peach diseases identification. To experiment with the developed algorithm, 2,657 images of diseased and non-diseased (healthy) peaches leaves are collected from the PlantVillage dataset. Using the integrated CNN-F with different models like ResNet-34, AlexNet, VGG16, and ResNet-50, the result accuracy rate has reached 94.12%, 91.12%, 92.14%, 93.23% respectively. Therefore, the application of CNN-F with ResNet-34 provides the best performance to identify the diseased leaves of peaches. This indicates that the developed algorithm can improvise the efficiency of peaches production by incorporating this algorithm to assist the farmers to cultivate healthy peaches.
Chinese Medical Named Entity Recognition Based on Pre-trained Language Models
Authors: Ruan Peiguang , Lv Wei , Li Mingxuan , Tang Weiyu 
Published: June 2023

Abstract
With the progressive development of academic research and the increasing standard of living, the need for medical entity identification is gradually increasing. Medical entity recognition has been rapidly developed to help build medical knowledge graphs and disease prediction, improve diagnostic accuracy, enable disease prevention, simplify clinical decision-making and reduce healthcare costs. In Chinese named entity recognition, the input is a word-level vector representation, but in Chinese text, words are the smallest units that express meaning. Although the BERT model has the advantage of excellent training results and can avoid the noise generated by the different word-level vector when the input word-level vector is used, it is wasteful for Chinese words that possess more information. Therefore, we propose a pre-training model based on Tag Embedding and Simple Lexicon word strengthening with word information fusion, which incorporates the word boundary information of the text into the more expressive text after encoding, increasing the amount of information it contains. The purpose of this study is to build a named entity recognition model using deep neural networks to improve the accuracy of medical-related entity recognition from the perspective of improving the information exploiting of medical data sets in the current era of big data informatics, and to contribute to related research.
Sputter-deposited α-MoO3 interlayers for van der Waals epitaxy and film transfer
Authors: Han Sangho , Lee Sangho , Ko Dohyun , Zhang Xinyuan , Kim Jeehwan , Ross Caroline , Kim Dong Hun 
Published: June 2023

Abstract
Integration of functional thin films onto flexible substrates is driven by the need to improve the performance and durability of flexible electronic devices. We present a van der Waals epitaxy technology that accomplishes the transfer of oxide or metal thin films via exfoliation or dissolution of sacrificial α-MoO3 layers produced by sputtering. The α-MoO3 thin films, consisting of weakly bonded two-dimensional layers, grew epitaxially on SrTiO3 (001) substrates, exhibiting mosaic domains rotated by 90°. Metallic Au films grown on the α-MoO3 were transferred by mechanical exfoliation or by dissolving the α-MoO3 in water at 45°C. Spinel-structured CoFe2O4 thin films grown on α-MoO3 layers were easily transferred to flexible substrates via mechanical exfoliation, and the magnetic anisotropy of the transferred CoFe2O4 films was modulated by bending.
Helping the voices of people with pancreatic cancer to be heard: a mixed media methodology to optimise recruitment for an international survey of pancreatic enzyme replacement
Authors: Landers Amanda , Cahill Charlotte , Poppy  , Kaw Anu , Brown Helen , McKenzie Clare , Agar Meera , Yenson Vanessa , Clarke Kate , Windsor John 
Published: June 2023

Abstract

Background: Pancreatic cancer is relatively rare and aggressive, with digestion and malabsorption issues often leading to significant weight loss. Recruitment of people with this malignancy into studies can be challenging and innovative methods need to be explored to improve recruitment rates.
Objective: To describe a mixed media methodology and the outcomes utilised to recruit patients to participate in a survey in a binational survey.
Setting: people with pancreatic cancer residing in Aotearoa New Zealand and Australia
Methods: The details of the mixed media method utilised to identify and recruit people with pancreatic cancer are described. This method was used to investigate pancreatic enzyme replacement therapy use in people with pancreatic cancer across Australia and Aotearoa New Zealand.
Results: The mixed media approach was successful in reaching 334 participants from a range of ethnicities and regions, representative of people with pancreatic cancer. Results showed that social media platforms were notably more efficient and cost-effective than radio and newspaper, but required additional expertise, including graphic design and media strategy knowledge.
Conclusions: Social media is an effective and efficient method of recruiting people with pancreatic cancer to a national survey. Studies utilising media to recruit patients may need to include team members with a range of skills.
Learning complex systems dynamics from vector fields over discrete measure spaces
Authors: Solis Ingrid Membrillo , Rossem Maria Van , Madeleine Tristan , Podoliak Nina , Orlova Tetiana , D'Alessandro Giampaolo , Brodzki Jacek , Kaczmarek Malgosia 
Published: June 2023

Abstract

A complex system is formed by entities that, through their interactions and dependencies, give rise to a unified whole with properties and behaviour distinct from those of its constituent parts. Examples of complex systems include the human brain, living cells, organisms, soft matter materials, the Earth's global climate, ecosystems and the economy. A large number of entities and dependencies produce high dimensional non-linear dynamics, which make the modelling, classification and prediction of complex systems dynamics a major challenge. Advances in modern information technology have made possible a successful use of data-driven approaches to the study of dynamical systems. However, in the particular case of complex systems there are still important open questions to address such as large and complex data sets analysis, dimensionality reduction, phase space reconstruction and global attractor classification. In this paper we introduce the theory of vector fields over discrete measure spaces to analyse data structurally complex, such as images, image gradients, and real and vector valued functions over simplicial complexes. We apply our framework to the analysis of data obtained from numerical solutions of equations commonly used in biology and physics to model a variety of complex systems. We show that our geometric framework, together with multidimensional scaling, an unsupervised learning method, can be successfully used in the analysis of large data sets for dimensionality reduction, mode decomposition and global attractor characterisation of complex systems dynamics. These results pave the way towards the characterisation and understanding of a number of complex dynamical systems.
Degradation of tetracycline by activated peroxodisulfate using a sulfur-modified iron-based material
Authors: Gao Shanxue , Zhang Nan , Chen Lei 
Published: June 2023

Abstract

The activation of persulfate by an iron-based catalyst presents a promising approach for the degradation of antibiotics; however, the activation efficiency remains a challenge. Herein, a sulfur-modified iron-based catalyst (S–Fe) by co-precipitation of sodium thiosulfate and ferrous sulfate in a molar ratio of 1:2 was prepared and the efficacy of the S–Fe/PDS system for the removal of tetracycline (TCH) was studied, where a higher removal efficiency was observed compared to the Fe/PDS system. Moreover, the effects of TCH concentration, PDS concentration, initial pH, and catalyst dosage on TCH removal were evaluated, and the highest efficiency was about 92.6% within a 30 min reaction time using a catalyst dosage of 1.0 g/L, a PDS dosage of 2.0 g/L, and a solution pH value of 7. The products and degradation pathways of TCH were analyzed by LC–MS. The free-radical-quenching experiments revealed that both SO4− • and •OH radicals contributed to the degradation of TCH in the S–Fe/PDS system, with the former playing a more significant role. The S–Fe catalyst also showed good stability and reusability for the removal of organic pollutants. Our findings suggest that the modification of an iron-based catalyst offers an effective approach to activate persulfate for removal of tetracycline antibiotics.
A Complete Sojourn of Gene Therapy along with its Targeting Approaches
for the Treatment of the Major Depressive Disorder
Authors: Gupta G.D. , Singh Dilpreet 
Published: June 2023

Abstract:
Approximately 2% to 3% of men and 6% to 7% of women suffer from severe depressive
disorders. The existing drugs only partially relieve symptoms for roughly 40% of these patients. The
majority of antidepressant drugs are based on theories that are now 50 to 60 years old, and the sector is
in critical need of new drug development targets. In the recent decade, numerous genes have been
connected to depression in animal models, and serious depression does run in families in humans, indicating
both a genetic and environmental component. Depression has been linked to the malfunctioning
of serotonin signaling genes, including p11, SERT, etc, according to earlier research. Gene therapy
for depression has been found in some instances to be relatively safe, despite the fact that it may seem
riskier and more invasive than medication. Hence, there is a growing field regarding the safest delivery
mechanisms of these genes that treat major depressive disorders permanently. Hence, the present
review summarized the delivery mechanisms of various genes responsible for depressive disorders
along with their molecular mechanisms and delivery at the cellular level.
The Rate of Insulin use and Suboptimal Glycemic Control among
Egyptian Patients with T2DM: Cohort Analysis of Eighth Wave of the
International Diabetes Management Practices Study (IDMPS)
Authors: Khalil Samir Helmy Assaad , Khaled Mohsen , Zakhary Raafat , Shereen Mark 
Published: June 2023

Aims:
The International Diabetes Management Practices Study (IDMPS) is an international
annual survey aiming to study and characterize the current standards of care for managing
DM in developing countries.

Background:
In Egypt, DM represents a substantial burden on the healthcare system, with an estimated
10.9 million patients, ranking it 10th amongst countries with the highest prevalence of DM.
Previous studies showed that to maintain safety and achieve treatment goals among diabetic patients,
optimal insulin therapy should be selected individually based on the patient’s needs. We reported
the proportion of Egyptian T2DM patients on insulin therapy who participated in the eighth
wave of the IDMPS.

Methods:
The 2018 IDMPS wave consisted of cross-sectional and longitudinal phases and aimed
to evaluate the proportion of T2DM who were on insulin therapy in 13 countries from four regions.
In Egypt, 17 physicians agreed to participate in the present study and were required to include at
least one patient.

Results:
A total of 180 T2DM patients were included in the cross-section phase. At the end of the
ninth month of follow-up, data from 170 T2DM patients were available. A total of 39 T2DM patients
(21.7%) were on insulin therapy, with a mean duration of 32.4 ± 36.6 months. More than half
of the patients (n = 22; 56.4%) were on basal insulin, mainly long-acting (n = 20; 90.9%). The
mean basal insulin daily dose was 0.3 ± 0.1 IU/Kg. Notably, 28.2% of the patients received insulin
via vials, and 46.2% stated that they were adjusting the insulin dose by themselves. On the other
hand, 60.2% of the study population was on oral antidiabetic drugs at the cross-sectional phase.
Nearly 17.4% and 27% of the patients in the cross-sectional phase achieved the glycemic target per
recommendations of international guidelines and the treating physicians, respectively. At the end
of the longitudinal phase, the percentage of T2DM patients who achieved glycemic targets increased
to 38.4% and 77.4% as per recommendations of international guidelines and the treating
physicians, respectively. Overall, 38.3% of T2DM patients received diabetes education, and 28.9%
were involved in an educational program provided by the physician or their clinical staff. Besides,
85.5% of T2DM patients followed their diabetes medication dosage and frequency strictly as prescribed.

Conclusion:
The proportion of insulin use in patients with T2DM aligned with the previous studies
from different countries; however, it is still inadequate to achieve the targeted glycemic control.
Nearly one-third of Egyptian patients received diabetes education, highlighting the need for adopting
a national educational program. Nonetheless, the level of adherence among T2DM from Egypt
appears to be high.