bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Intensity and Susceptibility of Gully Erosion through Multiple Discriminate Factors Analysis using Geospatial Techniques in Birbhum District of Rarh Region of West Bengal
Authors: Mondal Biraj Kanti , Basu Tanmoy , Lee Ming An 
Published: November 2022

Abstract
The present study focuses on the identification of the intensity and susceptibility of gully erosion and induced soil erosion in the selected areas of Birbhum district of West Bengal, India. The geospatial techniques have been employed using the meticulous remotely sensed satellite data of Birbhum district. The composite gully erosion susceptibility maps have been prepared based on the aggregation of the normalized values of each indicator using factor analysis of the year 2001. It reveals from the maps that Rampurhat-I and Bolpur-Santiniketan blocks of Birbhum district have faced an excessive loss of lateritic soil cover mostly by the gully erosion during the monsoon season concerning the relationship between soil erosivity and other factors. About 23.45 percent and 20.59 percent area of the total area under Rampurhat-I and Bolpur-Santiniketan blocks are under gully erosion susceptibility zones. The LULC and landholding sizes are despoiled with an increasing rate of soil erosion in the gully areas during the study period. The identification of gully erosion hot spot areas, intensity mapping, and accurate prediction will help to formulate highly insightful, well-developed strategies for adaptation of imperative planning to rectify the soil loss, and soil degradation and assist in eco-efficient agriculture and improves the agri-environmental conditions.
Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits
Authors: Scully Jack , Bourahmah Jassem , Bloom David , Shilnikov Andrey 
Published: November 2022

Abstract
The purpose of this paper is trifold -- to serve as an instructive resource and a reference catalog for biologically plausible modeling with i) conductance-based models, coupled with ii) strength-varying slow synapse models, culminating in iii) two canonical pair-wise rhythm-generating networks. We document the properties of basic network components: cell models and synaptic models, which are prerequisites for proper network assembly. Using the slow-fast decomposition we present a detailed analysis of the cellular dynamics including a discussion of the most relevant bifurcations. Several approaches to model synaptic coupling are also discussed, and a new logistic model of slow synapses is introduced. Finally, we describe and examine two types of bicellular rhythm-generating networks: i) half-center oscillators ii) excitatory-inhibitory pairs and elucidate a key principle -- the network hysteresis underlying the stable onset of emergent slow bursting in these neural building blocks. These two cell networks are a basis for more complicated neural circuits of rhythmogenesis and feature in our models of swim central pattern generators.
Analyzing the Potential of Active Learning for Document Image Classification
Authors: Saifullah Saifullah , Agne Stefan , Dengel Andreas , Ahmed Sheraz 
Published: November 2022

Abstract
Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well-known, however, that deep learning models are data-hungry and often require huge volumes of annotated data in order to achieve competitive performances. And since data annotation is a costly and labor-intensive process, it remains one of the major hurdles to their practical deployment. This study investigates the possibility of using active learning to reduce the costs of data annotation in the context of Document Image Classification, which is one of the core components of modern document processing pipelines. The results of this study demonstrate that by utilizing active learning (AL), deep document classification models can achieve competitive performances to the models trained on fully annotated datasets, and in some cases, even surpass them by annotating only 15%-40% of the total training dataset. Furthermore, this study demonstrates that modern AL strategies significantly outperform random querying, and in many cases achieve comparable performance to the models trained on fully annotated datasets even in the presence of practical deployment issues such as data imbalance, and annotation noise, and thus, offer tremendous benefits in real-world deployment of deep document classification models. The code to reproduce our experiments is publicly available at https://github.com/saifullah3396/doc_al.
Biomedical Knowledge Discovery From Unstructured Text Corpora Using Contextual Word Embeddings
Authors: Panesar Sandip S. 
Published: November 2022

Abstract
Background: Unsupervised extraction of knowledge from large, unstructured text corpora presents a challenge. Mathematical word embeddings taken from static language models such as Word2Vec have been utilized to discover "latent knowledge" within such domain-specific corpora. Here, semantic-similarity measures between representations of concepts or entities were used to predict relationships, which were later verified using domain-specific scientific techniques. Static language models have recently been surpassed at most downstream tasks by pre-trained, contextual language models like BERT. Some have postulated that contextualized embeddings potentially yield word representations superior to static ones for knowledge-discovery purposes. To address this question, two biomedically-trained BERT models (BioBERT and SciBERT) were used to encode n = 500, 1000 or 5000 sentences containing words of interest extracted from a biomedical corpus. The n representations for the words of interest were subsequently extracted and then aggregated to yield static-equivalent word representations for words belonging to vocabularies of biomedical intrinsic benchmarking tools for verbs and nouns. Using intrinsic benchmarking tasks, feasibility of using contextualized word representations for knowledge discovery tasks can be assessed: Word representations better encoding described reality are expected to demonstrate superior performance.
Results: The number of contextual examples used for aggregation had little effect on performance, however embeddings aggregated from shorter sequences outperformed those from longer ones. Performance also varied according to model used, with BioBERT demonstrating superior performance to static models for verbs, and SciBERT embeddings demonstrating superior performance to static embeddings for nouns. Neither model outperformed static models for both nouns and verbs. Moreover, performance varied according to model layer from which embeddings were extracted from, and depending upon whether a word was intrinsic to a particular model's vocabulary or required subword decomposition.
Conclusions: Based on these results, static-equivalent embeddings obtained from contextual models may be superior to those from static models. Moreover, as n has little effect on embedding performance, a computationally efficient method of sampling a corpus for contextual examples and leveraging BERT's architecture to obtain word embeddings suitable for knowledge discovery tasks is described.
Does healthcare coverage affect the decision to undergo female sterilization in a tertiary hospital setting?
Authors: Uaamnuichai Sutira , Phutrakool Phanupong , Thammasitchai Nareerat , Santhiloetsakun Sarochinee , Santibenchakul Somsook , Jaisamrarn Unnop 
Published: November 2022

Abstract
Background
Postpartum sterilization in Thailand has relatively few barriers and is partially covered by different healthcare plans. We aim to determine if healthcare plans or other demographic factors affect sterilization rate in a tertiary urban hospital.
Methods
We conducted a secondary analysis of a cohort of 4,482 postpartum women from previous data comparing contraception during and before the COVID-19 pandemic.
Results
Overall immediate postpartum sterilization rate was 17.8%. Those whose healthcare covered the procedure trended toward postpartum sterilization (OR = 1.11; 95%CI = 0.94–1.31). Women aged 40–44 years were 8.53 times as likely to choose sterilization than those aged 20–24 years (aOR = 2.74; 95%CI = 1.63–4.60). Women who delivered via cesarean section were more likely to undergo sterilization at the time of delivery (aOR = 5.78; 95%CI = 4.69–7.12). Women having their second or third child were more likely to undergo postpartum sterilization than those having their first child.
Conclusions
In conclusion, we found that age, parity, and route of delivery were associated with higher sterilization rates. Healthcare coverage was not associated with increased immediate postpartum sterilization in our setting.
MixLoss: A Data Augmentation Method for Sequence Generation Tasks
Authors: Chen Yaqi , Jiao Xiaolin , Zhang Hao , Qu Dan , Yang Xukui 
Published: November 2022

Abstract
In this paper, we propose MixLoss, a simple and effective data augmentation method for sequence generation tasks like machine translation, speech recognition, etc. MixLoss works by mixing two inputs and two losses with two corresponding outputs using the same weights, which can also be applied in the hidden space. Concretely, when the loss takes the certain form that we define, we prove that MixLoss is theoretically equivalent to Mixup and thus has an adequate theoretical foundation. We corroborate our theoretical analysis with experiments that support our conclusions. Experimental results on the English-Germany dataset reveal that MixLoss achieves the best performance compared with other data augmentation methods, and can integrate well with other technologies. Specifically, the BLEU can be improved from 27.10 to 28.41 on the MT task, and the WER can be decreased from 18.9% to 15.1% on the ASR task. Moreover, MixLoss has stronger robustness to resist noisy data.
On the identification of some species of the Cassieae tribe harvested in Cameroon using three Machine Learning technics
Authors: Fonkou Michèle Flore Yimga , Kengne William , Priso Richard Jules , Fono Louis Aimé , Ndongo Din 
Published: November 2022

Abstract
Species from the Cassieae tribe are widely used as ornamental, medicinal and food plants despite their apparent similarities. In this paper, we study identification of these species by means of the description of their characteristics and by using three machine learning methods (Decision Tree, k-Nearest Neighbors and Support Vector Machine). For that, we collect, in the cities of Douala and Yaoundé in Cameroon, a set of 390 specimens (13 species and 30 per specie) and we describe each of them based on 24 variables (23 features variables and one target variable given the name of the specie). These algorithms are implemented on the obtained database by simple cross validation and 10-folds cross-validation, the performance of each of them was evaluated by means of four indicators : the error rate/accuracy of the model, the sensitivity, the specificity and the Area under the ROC curve (AUC). The minimum accuracy is 95.4% obtained with 10-folds cross-validation. These algorithms perform better on the balanced dataset than on the unbalanced dataset.
Growth kinetics and purifying performance of A. Platensis in stirring-free culture based on brewery effluent's supernatant
Authors: Liady Mouhamadou Nourou Dine , Adande Richard , Virgile Akowanou Onésime Akuemaho , Adjahouinou Dogbè Clément , Cécilia Ahondokpè , Goussanou Baruc Kpèhouénou , Zouhir Fouad , Kouadio Louis Amani , Aïna Martin Pépin , Fiogbe Emile Didier 
Published: November 2022

Abstract
Due to costs of setting up and operating electrical stirring systems to keep algae in suspension and exposed to light, cultivation of monospecific algae is poorly expanded in developing countries. However, some algal species, such as Arthrospira platensis, are equipped with gaseous vesicles that allow them to stay afloat and increase their exposure to light. In this study, we investigated in an unstirred outdoor environment, its growth kinetic and purifying performance in a brewery effluent-based media. Batch cultures were carried out in three experimental treatments and evolution of physicochemical and growth parameters were monitored. Then its contribution to depollution was determined. Results show that optimal conditions for producing A. platensis include the culture tank transparency, the effluent dilution (i.e. 10%), and the culture media amendment with sodium bicarbonate and sodium nitrate. The average productivity recorded reached 0.55 g DW·L−1·d−1 during the exponential growth phase, while preserving culture from contamination. COD and total nitrogen concentrations were reduced to 32.5 and 64.91%. Such results open up prospects for low-cost production of certain algae, in transparent and relatively high barrels, thus breaking the classic barriers related to shallow basin depth and mechanical agitation traditionally considered as critical to the success of algal production.
Role of Flavonoids in Management of Various Biological Targets in Alzheimer’s Disease: Evidence from Preclinical to Clinical Studies
Authors: Alharbi Khalid Saad , Javed Shaikh Mohammad Arshad , Imam Syed Sarim , Alshehri Sultan , Ghoneim Mohammed M. , Almalki Waleed Hassan , Singh Sachin Kumar , Kumar Deepak , Kumar Avvaru Praveen , Dua Kamal , Chellappan Dinesh Kumar , Paudel Keshav Raj , Gupta Gaurav 
Published: November 2022

Abstract:More than 10 million people worldwide have Alzheimer's disease (AD), a degenerative neurological illness and the most prevalent form of dementia. AD's progression in memory loss, cognitive deterioration, and behavioral changes are all symptoms. Amyloid-beta 42 (Aβ42), the hyperphosphorylated forms of microtubule-associated tau protein, and other cellular and systemic alterations are all factors that contribute to cognitive decline in AD. Rather than delivering a possible cure, present therapy strategies focus on reducing disease symptoms. It has long been suggested that various naturally occurring small molecules (plant extract products and microbiological isolates, for example) could be beneficial in preventing or treating disease. Small compounds, such as flavonoids, have attracted much interest recently due to their potential to alleviate cellular stress. Flavonoids have been proven helpful in various ways, including antioxidants, anti-inflammatory agents, and anti-apoptotic agents, but their mechanism remains unknown. The flavonoid therapy of Alzheimer's disease focuses on this review, which includes a comprehensive literature analysis.
Clinical Application and Synthesis Methods of Deuterated Drugs
Authors: Xu Xiao-Liang , Zhang Wen , Rao Guo-Wu 
Published: November 2022

Abstract:
Many drugs have adverse absorption, distribution, metabolism, and excretory
(ADME) properties that prevent their widespread use or limit their use in some indications.
In addition to preparation techniques and prodrug strategies, deuterium modification
is a viable method for improving ADME properties. Deuterated drugs have attracted
increasing attention from the pharmaceutical industry in recent years. To date, two deuterated
drugs have been approved by the FDA. In 2017, austedo was approved by the FDA
as a new drug for Huntington's disease in the United States, the first deuterium drug to be
marketed worldwide. Recently (June 9, 2021), donafinil has been listed in China; this result
has caused major pharmaceutical companies and the pharmaceutical industry to pay
attention to deuterium technology again. In addition, BMS-986165, RT001, ALK-001,
HC-1119, AVP-786 and other drugs are in phase III clinical studies, and some solid deuterium
compounds have entered phase I and II clinical trials. The deuterium strategy has
been widely used in pharmaceutical research and has become a hot spot in pharmaceutical
research in recent years. In this paper, the research and development of deuterated
drugs are reviewed, and the influence of deuterium modification on drugs, the advantages
of deuterium strategies and the synthesis strategies of deuterated drugs are mainly
introduced. Hoping to provide references for clinical application, the discovery of new
deuterium chemical entities and research and development of new deuterated drugs.
Development and Characterization of Bosentan Monohydrate-loaded
Self-Nano Emulsifying Drug Delivery System
Authors: Tonjam Russel , Kurmi Balak Das , Singh Dilpreet 
Published: November 2022

Aim/ Objective:
The present investigation aims to develop a solid Self-nano emulsifying Drug Delivery System for enhancing the solubility and in vitro characteristics of poorly soluble an-ti-hypertensive drug Bosentan Monohydrate.

Methods:
The selection of formulation components on the basis of equilibrium solubility and pseu-do-ternary phase titration studies revealed the suitability of Isopropyl myristate, Tween 80 and Pol-yethylene glycol as the lipidic excipients and their optimized concentration ranges resulted in a sta-ble microemulsion region.

Results:
The systematic optimization of the liquid SNEDD formulations of Bosentan was per-formed using in vitro tests and detailed characterization studies. The results revealed that the F4 formulation produces excellent results and satisfactory results in all the CQA of liquid SNEDDS. The optimized liquid SNEDD formulations exhibited globule size of less than 100 nm, high and negative values of zeta potential, quick self-emulsification rate, negligible phase separation, and a high degree of physical stability during thermodynamic evaluation studies. SEM revealed nanostructured particles with negligible aggregation.

Conclusion:
In vitro dissolution studies of Bosentan in optimized liquid SNEDDS (F4) unveiled a multi-fold enhancement in release profile, as compared to pure API.
INFLUENCE OF PRODUCT QUALITY AND PRICE ON PURCHASE DECISIONS THROUGH WORD OF MOUTH AS INTERVENING VARIABLES
Authors: Asmaria Putri Della , Hidmi Shafitri Azizah 
Published: November 2022

ABSTRACT
This study aims to determine how much influence product quality and price have on purchasing decisions through word of mouth as an intervening variable (a case study on the kitchen culinary of mbok ghenduk purus in the city of Padang). This study uses a population of visitors with 98 samples obtained through purposive sampling method. This study uses multiple linear regression analysis. The results of this study indicate that there is a positive and significant effect on product quality on word of mouth, price has a positive and significant effect on word of mouth, product quality has a positive and significant effect on purchasing decisions, prices have a positive and significant effect on purchase decisions, word of mouth has an effect positive and significant impact on purchasing decisions, the indirect effect of product quality on purchasing decisions through word of mouth is greater than the direct effect of product quality on purchasing decisions, so it can be concluded that word of mouth is able to become an intervening variable between product quality and purchasing decisions. Direct price on purchasing decisions through word of mouth is greater than the direct effect of price on purchasing decisions, so it can be concluded that word of mouth can be an intervening variable between price and purchase decisions. Finally, the authors suggest that purchasing decisions can affect product quality and price intervening with word of mouth.
Research of senior high school students’ economic culture formation in the conditions of development of the digital education environment
Authors: Grass Tatiana P. , Petrishchev Vladimir I. 
Published: November 2022

The work is devoted to the problem of finding approaches to assessing the level of formation of economic culture among high school students, as one of its important components. Recognition of the significance of the phenomenon in question in the educational sphere and the realization that the system of pedagogical support of continuous economic education in educational organizations is, somewhat, outdated requires their essential updating. In this regard, the problems of finding approaches to the study of ways of senior high school students’ economic culture formation in the context of the development of the digital educational environment of the region are becoming urgent, which requires the construction of the model itself. The aim of the work is to study, analyze the state and model of the process senior high school students’ economic culture formation. The main research methods are theoretical analysis of scientific, psychological and pedagogical literature on the research problem, study and generalization of experience on the problem; observation, questioning, conversation, qualimetry, testing, method of expert assessments; ascertaining and formative experiment; analysis of the study; methods for mathematical statistical processing, statistical processing empirical data. The study was conducted on the basis of Krasnoyarsk State Pedagogical University named after V. P. Astafiev with bachelor’s and master’s degree students, high school students, teachers from educational institutions, students of universities and secondary professional educational institutions of Krasnoyarsk and Krasnoyarsk Territory. The analysis of the state of the problem of high school students’ economic culture formation based on the diagnosed test tools revealed correctional dependences between the indicators of four components in the structure of high school students economic culture formation and the levels of this formation under the conditions of digital education environment development in the region. The obtained dependencies and research data made it possible to form a model of this process in the context of the development of the digital educational environment of the region, describing the degree of formation of each component at each level.
Revitalisasi Prinsip Tabayyun dan Qaula Sadida
Authors: Munawarah Munawarah 
Published: November 2022

Pandemi COVID-19 sejak awal disertai dengan fenomena lainyang disebut dengan “infodemi”. Istilah ini merujuk pada ‘wabah’ yang berupa informasi palsu dan menyesatkan. Menyebarkan informasi palsu merupakan perbuatan yang dilarang dalam Islam, karena berpotensi menciptakan kerancuan informasi (misinformation), yang beakibat pada kerancuan persepsi dan tindakan (misleading perception and action). Allah melalui QS. Al-Isra ayat 36, QS. Al-Hujurat ayat 6, QS.An-Nisa/4:9 dan QS. Al-Ahzab/33:70 secara keseluruhan berpesan agar tidak mudah mengikuti dugaan, prasangka ataupun informasi-informasi yang tidak dapat diandalkan kebenarannya. Dalam artikel ini, penulis berusaha merevitalisasi prinsip tabayyun dan Qaulan Sadida yang telah diisyaratkan oleh Alquran, sebagai upaya strategis untuk mengatasi infodemi di era pandemi ini.
Penentuan Cadangan Premi pada Asuransi Jiwa Berjangka Status Joint Life Menggunakan Metode Canadian
Authors: Ibrahim Fira Septiyani 
Published: November 2022

The Premium reserves is an obligation that a life insurance company must have to provide compensation to the heirs if there is a claim. This research aims to create a premium reserves in term life insurance with joint life status. This research is a theoretical study that examines about the calculation of the joint life status insurance premium reserves using the Canadian method using the 2019 Indonesian Mortality Table (TMI) and the case application sample. The calculation of reserves begins by forming a table of mortality and joint commutation of two insured persons, forming a formula for combined life annuity, single net premium, and annual net premium. So that in this research, the formulation of reserves for term joint life insurance premiums with the Canadian method is obtained