bluuebunny/crossref_metadata_2025_split huggingface dataset:
ISOLASI DAN KARAKTERISASI ASAM HUMAT DARI TANAH GAMBUT
Authors: Rahmawati Atik 
Published: December 2019

Telah dilakukan penelitian tentang isolasi dan kara- kterisasi asam humat hasil isolasi tanah gambut yang be- rasal dari Desa Sambutan, Kecamatan Samarinda Ilir, Sa- marinda, Kalimantan Timur.Isolasi asam humat dari tanah gambut menggunakan metode ekstraksi alkali, pemurnian dan penentuan kand- ungan gugus fungsional asam humat secara titrasi poten- siometri.Hasil penelitian menunjukkan gugus fungsional utama asam humat hasil isolasi tanah gambut adalah gu- gus –COOH dan gugus –OH. Secara kuantitatif kandungan gugus fungsional tersebut masing-masing sebesar 426,2 cmol/kg (gugus –COOH), 289,6 cmol/kg (gugus –OH fenolat) dan 37,0 cmol/kg (gugus –OH alkoholat).
PEMBUATAN ALAT LABORATORIUM FISIKA FAKULTAS ILMU TARBIYAH DAN KEGURUAN IAIN WALISONGO UNTUK PRAKTIKUM OSILASI CAIRAN DAN AYUNAN MATEMATIS SECARA DIGITAL
Authors: Sudarmanto Agus 
Published: December 2019

Penelitian ini merancang dan merealisasikan pembua- tan alat laboratorium Fisika Fakultas Tarbiyah IAIN secara digital yaitu pada praktikum osilasi cairan dan ayunan matematis.Pembuatan alat laboratorium Fisika Fakultas Ilmu Tar- biyah dan Keguruan IAIN Walisongo untuk praktikum osi- lasi cairan dan ayunan matematis secara digital terdiri dari sumber cahaya, sensor cahaya yang keluarannya, dengan keypad sebagai masukannya kemudian diproses dengan mikrokontroler ATMega8535 dengan algoritma program kemudian hasilnya ditampilkan pada LCD (Liquid Cristal Display).Hasil penelitian ini adalah bahwa pembuatan alat lab- oratorium sika untuk praktikum osilasi cairan dan ayu- nan matematis secara digital sudah dapat berfungsi sesuai dengan yang diinginkan dan data hasil dari alat tersebut mendekati nilai percepatan gravitasi bumi sebenarnya yai- tu sebesar 9,8 m/s2.
Klasifikasi Pemahaman Santri Dalam Pembelajaran Kitab Kuning Menggunakan Algoritma Naive Bayes Berbasis Forward Selection
Authors: Mahfudh Adzhal Arwani , Mustofa Hery 
Published: December 2019

Kitab kuning merupakan kitab tradisional yang mengandung diraasah islamiyah yang diajarakan pada pondok pesantren, mulai dari struktur bahasa arab (ilmu nahwu dan shorof), ‘ulumul qur’an, hadits, aqidah, tasawuf/akhlaq, tafsir, fiqh sampai ilmu sosial dan kemasyarakatan (mu’amalah). Disebut juga dengan kitab gundul karena tidak memiliki harakat (fathah, kasroh, dhammah, sukun) untuk bisa membaca dan memahami secara menyeluruh dibutuhkan waktu yang relatif lama. Penelitian ini bertujuan untuk mendapatkan model klasifikasi dari data pembelajaran kitab kuning di pondok pesantren. Metode yang digunakan dalam penelitian ini adalah <em>forward selection</em> sebagai praproses dalam mengurangi dimensi data, menghilangkan data yang tidak relevan dan naive bayes yang berguna untuk mengklasifikasi data. Hasil dari klasifikasi data pembelajaran kitab kuning menggunakan atribut yang telah diklasifikasi berdasarkan fitur-fiturnya dan dilakukan iterasi pada <em>cross validation </em>sehingga menghasilkan akurasi yang tepat. Berdasarkan hasil pengujian dengan dua metode, pengujian dengan algoritma Naive bayes saja menghasilkan akurasi 96,02%, untuk algoritma Naive bayes berbasis forward selection menghasilkan akurasi 97,38% . Terdapat peningkatan akurasi dengan penambahan fitur seleksi.
Fractional factorial design modelling on degradation of Direct Red 81 dye by advanced oxidation process – ozonation: reaction kinetics
Authors: Muniyasamy Abaranjitha , Sivaporul Gayathri , Gopinath Abarna , John Juliana , Achary Anant , Chellam Padmanaban Velayudhaperumal 
Published: December 2019

Abstract
In the present work, the degradation of Direct Red 81 by ozonation was studied. The interactive effects of the influencing factors (dye concentration = 500–2,000 mg/L; time = 10–30 min; pH = 7.0–11.0) on degradation efficiency was critically examined through experimental design optimization by central composite design under the response surface methodology. The high correlation coefficients (R2 = 0.976 & adjusted R2 = 0.958) obtained by analysis of variance (ANOVA) demonstrated close fit between the experimental and the predicted values. Optimized conditions under specified cost-driven restraints were obtained for the highest desirability (i.e. degradation of 1,210.59 mg/L dye) at pH = 11.0, initial dye concentration = 2,000 mg/L and ozone exposure time of 27.16 min. The degradation of Direct Red 81 was confirmed through Fourier transform infrared spectroscopy (FTIR) analysis and UV-Vis spectrometry.
In Silico Studies on Anti-Stress Compounds of Ethanolic Root Extract of Hemidesmus indicus L.
Authors: Daddam Jayasimha R. , Sreenivasulu Basha , Umamahesh Katike , Peddanna Kotha , Rao Dowlathabad M. 
Published: December 2019

Background:Alternative medicine is available for those diseases which cannot be treated by conventional medicine. Ayurveda and herbal medicines are important alternative methods in which the treatment is done with extracts of different medicinal plants. This work is concerned with the evaluation of anti-stress bioactive compounds from the ethanolic root extract of Hemidesmus indicus.Methods:Gas chromatography and Mass Spectrum studies are used to identify the compounds present in the ethanolic extract based on the retention time, area. In order to perform docking studies, Vasopressin model is generated using modeling by Modeller 9v7. Vasopressin structure is developed based on the crystal structure of neurophysin-oxytocin from Bos taurus (PDB ID: 1NPO_A) collected from the PDB data bank. Using molecular dynamics simulation methods, the final predicted structure is obtained and further analyzed by verifying 3D and PROCHECK programs, confirmed that the final model is reliable. The identified compounds are docked to vasopressin for the prediction of anti-stress activity using GOLD 3.0.1 software.Results:The predicted model of Vasopressin structure is stabilized and confirmed that it is a reliable structure for docking studies. The results indicated ARG4, THR7, ASP9, ASP26, ALA32, ALA 80 in Vasopressin are important determinant residues in binding as they have strong hydrogen bonding with phytocompounds. Among the 21 phytocompounds identified and docked, molecule Deoxiinositol, pentakis- O-(trimethylsilyl) showed the best docking results with Vasopressin.Conclusion:The identified compounds were used for anti-stress activity by insilico method with Vasopressin which plays an important role in causing stress and hence selected for inhibitory studies with phytocompounds. The phytocompounds are inhibiting vasopressin through hydrogen bodings and are important in protein-ligand interactions. Docking results showed that out of twenty-one compounds, Deoxiinositol, pentakis-O-(trimethylsilyl) showed best docking energy to the Vasopressin.
Cancer Cell-derived Secretory Factors in Breast Cancer-associated Lung Metastasis: Their Mechanism and Future Prospects
Authors: Urooj Tabinda , Wasim Bushra , Mushtaq Shamim , Shah Syed Nudrat Nawaid , Shah Muzna 
Published: December 2019

:
In Breast cancer, Lung is the second most common site of metastasis after the bone. Various
factors are responsible for Lung metastasis occurring secondary to Breast cancer. Cancer cellderived
secretory factors are commonly known as ‘Cancer Secretomes’. They exhibit a prompt role
in the mechanism of Breast cancer lung metastasis. They are also major constituents of hostassociated
tumor microenvironment. Through cross-talk between cancer cells and the extracellular
matrix components, cancer cell-derived extracellular matrix components (CCECs) such as
hyaluronan, collagens, laminin and fibronectin cause ECM remodeling at the primary site (breast) of
cancer. However, at the secondary site (lung), tenascin C, periostin and lysyl oxidase, along with
pro-metastatic molecules Coco and GALNT14, contribute to the formation of pre-metastatic niche
(PMN) by promoting ECM remodeling and lung metastatic cells colonization. Cancer cell-derived
secretory factors by inducing cancer cell proliferation at the primary site, their invasion through the
tissues and vessels and early colonization of metastatic cells in the PMN, potentiate the mechanism
of Lung metastasis in Breast cancer.

:
On the basis of biochemical structure, these secretory factors are broadly classified into proteins and
non-proteins. This is the first review that has highlighted the role of cancer cell-derived secretory
factors in Breast cancer Lung metastasis (BCLM). It also enumerates various researches that have
been conducted to date in breast cancer cell lines and animal models that depict the prompt role of
various types of cancer cell-derived secretory factors involved in the process of Breast cancer lung
metastasis. In the future, by therapeutically targeting these cancer driven molecules, this specific
type of organ-tropic metastasis in breast cancer can be successfully treated.
Lentiviral-Mediated Beclin-1 Overexpression Inhibits Cell Proliferation and Induces Autophagy of Human Esophageal Carcinoma Eca109 Cell Xenograft in Nude Mice
Authors: Zhang Junhe , Dong Weihua 
Published: December 2019

Background:
Esophageal carcinoma is one of the common malignant tumors in digestive
tract. BECLIN-1 is a key gene that regulates autophagy, and its abnormal expression may be related
with many human tumors. However, the mechanism of BECLIN-1 in esophageal carcinoma remains
unknown.

Objective:
In this study, we explored the effect of BECLIN-1 overexpression on tumor growth in mice
with esophageal carcinoma and its mechanism.

Methods:
Recombined lentiviral vector containing BECLIN-1 was used to transfect human esophageal
carcinoma Eca109 cells and establish stable cell line. qRT-PCR was used to detect BECLIN-1 mRNA
level in the transfected Eca109 cells, CCK-8 assay was used to detect cell proliferation. Beclin-1, P62
and LC3-II protein expression levels in Eca109 cells were detected using Western blot analysis. Subcutaneous
xenograft nude mice model of human esophageal carcinoma was established, and the tumor
growths in Beclin-1 group, control group and empty vector group were monitored. Beclin-1 protein
expression in vivo was detected by immunohistochemistry.

Results:
Beclin-1 mRNA and protein were overexpressed in Eca109 cells. Compared with empty vector
group, the growth rate of cells transfected with BECLIN-1 decreased significantly. Compared with
the control group and empty vector group, the expression level of P62 protein in beclin-1 group was
significantly decreased, while the expression level of LC3-II protein was significantly increased. The
tumor growth rate in nude mice of Beclin-1 group was significantly lower than that of the control
group and empty vector group, and Beclin-1 protein was mainly expressed in Beclin-1 group in vivo.

Conclusion:
BECLIN-1 can induce autophagy in esophageal carcinoma Eca109 cells, and it can significantly
inhibit the growth of esophageal carcinoma.
Cisplatin and Probiotic Biomass Loaded Pessaries for the Management of Cervical Cancer
Authors: Negi Deepa , Singh Amandeep , Joshi Navin , Mishra Neeraj 
Published: December 2019

Introduction:
Cancer is a type of disease, in which the growth of cells is abnormal and uncontrolled.
One of the most common cancers among women is cervical cancer. In India, cervical cancer is one of leading
causes of cancer mortality among women 30 to 69 years of age, accounting for 17% of all cancer deaths. The
work present here shows the combined effects of anticancer drug along with probiotics to circumvent the side
effects associated with chemotherapy and to enhance the therapeutic effect.

Material and Methods:
Cisplatin and drug loaded pessaries were prepared by melt mold method using the
blend of PEG’s (Polyethylene Glycol) and further characterized for various in vitro and in vivo parameters.

Result and Discussion:
The free radical scavenging activity of probiotic Lactobacillus rhamnosus by DPPH
(2,2-diphenyl-1-picrylhydrazyl) assay was observed to be 60.77μg/mL The mean weight variation, melting time,
content uniformity, friability and hardness of the prepared pessary were 1.25±0.025mg, 10.86±0.64min,
99.89±0.74, 0.25%, 2.2kg/cm2. Histopathology studies presented that the developed formulation are safe for
local delivery of cisplatin.

Conclusion:
This study provides the basis for a combination of local delivery approach along with the beneficial
effects of probiotic strain which could be better a approach for the treatment of cervical cancer.
Photonic Crystal Based Bio-Sensor Detection in Nanophotonic Structure Using FEM Method
Authors: Ghoumazi Mehdi , Hocini Abdesselam 
Published: December 2019

Background & Objective:
In this article, a nano-cell coupled 2D photonic crystal
waveguide bio- detection platform is proposed for the detection of organic liquids such as water,
ethanol, glycerol, benzene and bromine for different refractive indices (n).

Methods:
The detection characteristics are analyzed and obtained for the band diagram, before and
after change of radius (r) as well as after the injection of liquids into the heart of the nanostructure
cell at the resonant wavelength (λ res). Also, we extracted variations from the power flow norm (P)
to the resonance and the transmission (T) for each material. This nanostructure for different (n) materials
used, gives (P) at different levels to the resonance. This explains the importance of the refractive
index parameter (n) which plays a crucial role in the detection of materials.

Results & Conclusion:
The proposed biosensor enables the detection of liquids. This is shown
through the numerical results obtained. These results give a good observation of the different behavior
of the signal in the presence of the liquids used which depend on their refractive indices 'n'. This
parameter 'n' is very important for detection when it varies from material to material. The proposed
biosensor can be used in several research areas, particularly in medical and environmental applications.
Intuitionistic Fuzzy Score Function Based Multi-Criteria Decision Making Method for Selection of Cloud Service Provider
Authors: Agrawal Sonal , Tripathi Pradeep 
Published: December 2019

Aims & Background:
Cloud Computing (CC) has received great attention from the
scholarly researchers and IT companies. CC is a standard that offers services through the Internet.
The standard has been manipulated by existing skills (such as collect, peer-to-peer and grid computing)
and currently accepted by approximately all major associations. Various associations like
as Microsoft and Facebook have revealed momentous investments in CC and currently offer services
with top levels of reliability. The well-organized and precise evaluation of cloud-based
communication network is an essential step in assurance both the business constancy and the continuous
open services.

Objectives & Methods:
To select and rank the CC service providers, we introduce an Improved
Score Function (ISF) based Multi-Criteria Decision-Making (MCDM) approach. The proposed
approach is developed to solve the MCDM problems with partly unknown weight. To do this, the
criteria preferences are given in terms of Intuitionistic Fuzzy Sets (IFSs). Numerical example is
illustrated to show the effectiveness of the proposed approach over the previous ones.

Results:
A decision making problem of cloud computing service provider has been considered for
signifying the developed technique and finishes with the outcomes coincide with the already developed
methods which confirms the solidity of the developed method.

Conclusion:
For future, we plan to implement the proposed technique on various decision making
problems, clustering and multi-objective problems. Also, we plan to extend our method under different
uncertain atmosphere by using other MCDM methods.
Isolated Word-Based Spoken Dialogue System Using Odia Phones
Authors: Swain Basanta K. , Mohanty Sanghamitra , Chowdhary Chiranji L. 
Published: December 2019

Aims:
To develop Spoken Dialogue System in Indian language with Voice response and
voice based biometric feature.

Backgrounds:
Most of research works in spoken dialogue system are carried out in U.S. and Europe
and currently, few government funding projects on spoken dialogue system (SDS)
are carried out in Indian academic institutes.

Objective:
We have tried to use our developed spoken language system to eliminate the desktop
clutter. It is very normal tendency of computer users to place the most frequently used files, folders,
applications shortcuts on their computer’s desktop. Cluttering of desktop not only slows down
the productivity of computer but also looks very messy and very difficult to find files as well.
Therefore, we tried to use the spoken dialogue system to eliminate the desktop clutters in painless
manner and the services are provided to the computer users by opening the files, folders and frequently
used application of users in spoken command mode with voice response.

Methods:
In this research article, we have attempted to utilize an Indian spoken language for
communication with spoken dialogue system. We have adopted a statistical machine learning algorithm
called Hidden Markov Model for development of speech recognition engine. The speaker
verification module is developed using fuzzy c-means algorithm. Speech synthesis is carried out
using diphone corpus.

Results:
The speaker verification module has yielded satisfactory results with average accuracy of
66.2% using FCM algorithm. It is also seen that fundamental frequency and formant frequency carry
the distinctive characteristics of speaker verification over Indian spoken language. The vital
module of SDS i.e. speech recognition engine is developed by using HMM, a statistical algorithm.
It is observed that word accuracy of ASR engine is 78.22 % and 62.31 % for seen and unseen users
respectively. The voice response is given to the user in terms of synthesized speech. The audio
quality of synthesized speech is measured using the MOS test. The MOS test value is found as 3.8
and 3.6 over two distinct groups of listeners.

Conclusion:
In this research paper, we have developed a spoken dialogue system based on Odia
language phone set. We have integrated speaker verification module in order to provide additional
biometric based security.
Improving Data-Throughput in Energy Harvesting Wireless Sensor Networks Using a Data Mule
Authors: Kumar Naween , Dash Dinesh 
Published: December 2019

Background:
In Energy Harvesting Wireless Sensor Networks (EH-WSNs), sensors are
harvesting energy from the renewable environment to make their operations endless and uninterrupted.
However, in such a network, the time-varying nature of harvesting imposes a challenging
issue in obtaining improved data-throughput. The use of a static-sink in EH-WSNs to improve data-
throughput is less reliable because there is no assurance of the network connectivity. To alleviate
such shortcomings, a Data Mule (MDM) has been introduced in EH-WSN for collecting sensors’
data. In this article, the MDM-based distance constrained tour finding problem is formulated
such that the data-throughput can be improved within a given delay constraint.

Methods:
To solve the problem, we devise two different heuristic algorithms based on two different
metrics.

Results:
The obtained experimental results demonstrate that the devised algorithms are more effective
than the existing algorithms in terms of data-throughput.

Conclusion:
The data-throughput values of the first proposed algorithm are about 6.14% and
3.56% better than the other for two different data gathering time durations of 100 sec and 800 sec.
The data-throughput values of the second proposed algorithm are about 5.03% and 5.25% better
than the other for two different data gathering time durations of 100 sec and 800 sec.
Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies
Authors: Moon Sun Jae , Hwang Jinseub , Kana Rajesh , Torous John , Kim Jung Won 
Published: December 2019

Background
In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy.

Objective
This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD.

Methods
The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC).

Results
A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67.

Conclusions
The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings.

Trial Registration
PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779