bluuebunny/crossref_metadata_2025_split huggingface dataset:
Groundwater prospect mapping using remote sensing, GIS and resistivity survey techniques in Chhokra Nala Raipur district, Chhattisgarh, India
Authors: Indhulekha K. , Chandra Mondal Kamalesh , Jhariya D. C. 
Published: August 2019

Abstract
Groundwater availability in urbanized areas is under high demand due to overconsumption and lack of recharge area. It is important to consider the groundwater scenario of the cities and industrial areas for its safe consumption and management. In this framework, remote sensing, geographic information system (GIS), is a tool which plays a vital role to map groundwater prospect zones due to its convenience and time-saving nature. The present study area, the watershed of Chhokra Nala, covers an area which consists partly of an industrial area and also Raipur city. The current study has utilized satellite imagery, along with other data sets, to develop different thematic layers such as geology, land use land cover, drainage and drainage density, lineament, geomorphology, rainfall, slope, groundwater depth and soil types. Integration of all these thematic layers through GIS analysis delineated the groundwater prospect zones by the application of a weighted index overlay method. A Resistivity Survey was also performed to locate groundwater potential zones. The groundwater potential zone map of the study area is categorized into five different zones, namely very low, low, moderate, high and very high.
Improving operational management of wastewater systems. A case study
Authors: Pereira A. , Pinho J. L. S. , Faria R. , Vieira J. M. P. , Costa C. 
Published: August 2019

Abstract
Wastewater treatment facilities collecting wastewater from longstanding sewer networks of five municipalities in the Ave River basin (located in NW Portugal) are especially vulnerable to water inflows since they have considerable extensions of sewers installed in stream and riverbeds. TRATAVE, the company responsible for operating the system, designed and implemented a monitoring network to measure discharges along the entire drainage network and treatment facilities in order to reduce those water inflows. Several flow measurement devices were installed at strategic locations within the sewer network and integrated with a SCADA system responsible for its operation. A decision support system (DSS) is being implemented using the Delft-FEWS platform, integrating monitoring data and models. Based on monitored data and model results, an estimation of infiltration volumes during wet periods is presented. Moreover, the capabilities of the DSS are illustrated in: (i) location of manholes losses along sewer networks during wet periods; (ii) identification and location of unknown connections to the sewer network using wastewater balances; and (iii) design of a PID controller for a pumping station using on-line tank water level measurement. Acquired knowledge resulting from the DSS greatly improved the utility performance both in terms of economic revenue and environmental protection.
Neurological Involvement as the Initial Manifestation in Primary Sjögren's Syndrome - A Case Report
Authors: Posso-Osorio Iván , Naranjo-Escobar Juan , Loaiza Diana Marcela , Polo Marcela , Echeverri Alex , Tobón Gabriel J. 
Published: August 2019

Background:
Sjögren's syndrome is an autoimmune disease characterised by
exocrinopathy mainly involving the salivary and lacrimal glands. In addition, it is a multisystemic
condition (i.e., affecting multiple organs and systems). Neurological involvement has been reported
in ~20% of cases, with peripheral manifestations being the most frequent.

Methods:
We analysed four cases in which neurological manifestations were the first symptoms of
Sjögren's syndrome.

Results:
In all four cases, neurological symptoms preceded sicca symptoms. In addition, immunosuppressive
treatment with steroids and, in some cases, cyclophosphamide showed improvement.

Conclusion:
Neurological involvement in Sjögren's syndrome is common and often occurs as the
first clinical manifestation. Since evidence is limited, more studies are required in order to determine
appropriate diagnostic methods and treatments for each manifestation of Sjögren's syndrome.
Key Relevance of Epigenetic Programming of Adiponectin Gene in Pathogenesis of Metabolic Disorders
Authors: Un Nisa Kaiser , Reza Mohammad Irshad 
Published: August 2019

Background & Objective::
Significant health and social burdens have been created by the
growth of metabolic disorders like type 2 diabetes mellitus (T2DM), atherosclerosis, and non-alcoholic
steatohepatitis, worldwide. The number of the affected population is as yet rising, and it is assessed
that until 2030, 4−5 million individuals will acquire diabetes. A blend of environmental, genetic, epigenetic,
and other factors, such as diet, are accountable for the initiation and progression of metabolic
disorders. Several researches have shown strong relevance of adiponectin gene and metabolic disorders.
In this review, the potential influence of epigenetic mechanisms of adiponectin gene “ADIPOQ”
on increasing the risk of developing metabolic disorders and their potential in treating this major disorder
are discussed.

Results & Conclusion::
Various studies have postulated that a series of factors such as maternal High
fat diet (HFD), oxidative stress, pro-inflammatory mediators, sleep fragmentation throughout lifetime,
from gestation to old age, could accumulate epigenetic marks, including histone remodeling, DNA
methylation, and microRNAs (miRNAs) that, in turn, alter the expression of ADIPOQ gene and result
in hypoadiponectinemia which precipitates insulin resistance (IR) that in turn might induce or accelerate
the onset and development of metabolic disorder. A better understanding of global patterns of epigenetic
modifications and further their alterations in metabolic disorders will bestow better treatment
strategies design.
Withdrawal Notice: Intelligent Logistics System Optimization Based on IoT and Fuzzy Algorithms
Authors: Jiang Yanyan , Tian Shen 
Published: August 2019

The article has been withdrawn at the request of the author of the journal Recent Advances in Computer Science and
Communications.;

Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.

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the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for
publication.
METHWORK: An Approach for Ranking of Research Trends with a Case Study for IoET
Authors: Kumar Neeraj , Agrawal Alka , Khan Raess A. 
Published: August 2019

Objective:
Ranking in many areas has been a big problem for a long time. The authors
tried to use a novel ranking approach to find out the most popular research interest among all the research
fields. The authors have assumed that the selection of a research area is a tedious task.

Methods:
Therefore, they tried to propose a mechanism named METHWORK (Methodology With
the Opted Related Keywords) to choose popular research trends. Google-based searching was applied
to find samples in the initial state of this approach. The METHWORK was tested with respect
to a case of ranking of research trends within the IoET (Internet of Environmental Things). To find
ranks, the first phase is to assess the popularity of the topics in the existing published research papers.
To find out the correctness of the ranking found using METHWORK, the authors performed
χ2 hypothesis testing while making a comparison with current ranking techniques.

Results:
The methodology proposed is a milestone to find out the research trends within a broad research
area.

Conclusion:
The results of the test indicated that the approach could be applied to determine trends in
any research discipline and proved the applicability of the proposed approach.
Experimental and Comparison Based Study on Diabetes Prediction Using Artificial Neural Network
Authors: Pradhan Nitesh , Dhaka Vijaypal S. , Kulhari Satish C. 
Published: August 2019

Background:
Diabetes is spreading in the entire world. In a survey, it is observed that every
generation from child to old age people are suffering from diabetes. If diabetes is not identified in time,
it may lead to deadliest disease. Prediction of diabetes is of the utmost challenging task by machines. In
the human body, diabetes is one of the perilous maladies that creates depended disease such as kidney
disease, heart attack, blindness etc. Thus it is very important to diagnose diabetes in time.

Objective:
Our target is to develop a system using Artificial Neural Network (ANN), with the ability to
predict whether a patient suffers from diabetes or not.

Methods:
This paper illustrates various machine learning techniques in form of literature review; such
as Support Vector Machine, Naïve Bayes, K Nearest Neighbor, Decision Tree, Random Forest, etc. We
applied ANN to predict diabetes. In this paper, the architecture of ANN consists of four hidden layers each
of six neurons and one output layer with one neuron. Optimizer used for the architecture is ‘Adam’.

Results:
We have Pima Indian diabetes dataset of sufficient number of patients with nine different
symptoms with respect to the patients and nine different features in connection with the mathematical
computation/prediction. Hence we bifurcate the dataset into training and testing set in majority and minority
ratio of 80:20 respectively. It facilitates us the majority patient’s data to be used as training set
and minority data to be used as testing set. We train our network for multiple epoch with different activation
function. We used four hidden layers with six neurons in each hidden layer and one output layer.
On the hidden layer, we used multiple activation functions such as sigmoid, ReLU etc. and obtained
beat accuracy (88.71%) in 600 epochs with ReLU activation function. On the output layer, we used only
sigmoid activation function because we have only two classes in our dataset.

Conclusion:
Diabetes prediction by machine is a challenging task. So many machine learning algorithms
exist to predict the diabetes such as Naïve Bayes, decision tree, K nearest neighbor, support vector
machine etc. This paper presents a novel approach to predict whether a patient has diabetes or not
based on Pima Indian diabetes dataset. In this paper, we used artificial neural network to train out network
and it is observed that artificial neural network approach performs better than all other classifiers.
Big Data Analysis on Job Trends Using R
Authors: Ramasubbareddy Somula , Walia Anish S. , Luhach Ashish K. , Kannayaram Govinda , Evakattu Swetha 
Published: August 2019

Background:
Nowadays, the demand for data science-related job positions have seen a
huge increase due to the recent data explosion incurred by the industries and organizations globally.
The necessity to harness and utilize the amount of information hidden inside these huge datasets for
effective decision-making has become the need of the hour. However, this scenario is where a data
analyst or a data scientist comes into play. They are domain experts who have the skillset and expertise
to extract hidden meaning from data and convert them into useful insights. This work illustrates
the use of data mining and advanced data analysis techniques such as data aggregation, summarization
along with data visualization using R tool to understand and analyse the job trends in the United
States of America (USA) and then drill down to analyse job trends for data science-related job positions
from year 2011 to 2016.

Objective:
This paper discusses the general job trends in the US and how the job seekers are migrating
from one place to another place using Visa for different titles, majorly for business analytics.

Methods:
Analytics is done using R programming, different functions of the programming on various
parameters and inference is drawn on the result.

Results & Conclusion:
The aim of this analysis is to predict the job trends in line with demand, region,
employers, wages in USD.
ANALYSIS OF APPLICABILITY OF TOOLS OF INVENTORY CONTROL AND KNOWLEDGE OF HOSPITAL PHARMACISTS ON METHODS OF INVENTORY IN THE CENTRAL PHARMACY OF A TERTIARY CARE HOSPITAL
Authors: ASHWINI R. SAI , CHOWDRY B. SAI , SANNIHITHA B. SAI , RAJ V. SENTHIL , RANI N. VANITHA , P. THENNARASU , AIYAVOO MALARVIZHI 
Published: August 2019

Objective: A study was conducted to analyse the applicability of the tools of inventory control of drugs, and the awareness of hospital pharmacists on the inventory tools in the central pharmacy of a tertiary care hospital.
Methods: Always Better Control (ABC) and High Moderate Low (HML) analysis were carried out for 4057 drugs purchased in the central pharmacy during the financial year of 2017-2018. The drugs were classified as A, B and C category based on their annual usage value and as Category High (H), Moderate (M) and low (L) drugs based on their unit price respectively. The knowledge of the 80 hospital pharmacists on the tools of inventory control was assessed using a structured questionnaire.
Results: Based on ABC analysis, of 4057 drugs, 330 drugs were classified under “A” category accounting to 8.13% of the total number of drugs purchased which was 70.91 % of total annual consumption in money value. The category “B” consisted of 667 drugs accounting to 16.44% of the total number of drugs which was 20.01% of total annual money consumption. Category “C” had 3060 drugs which accounted to 75.42% of the total number of drugs purchased which reflected on 9.01% of annual money consumption. Based on HML analysis, 440 drugs (10%) had a unit price above Rs. 500 and were classified under ‘H’ category. The drugs with unit price between Rs.500 and Rs.100 were grouped as ‘M’ items. There were 695 drugs (17.13%) in ‘M’ category. The ‘L’ category consisted of 2922 drugs (72.02%) with their unit price below Rs.100. Of 80 pharmacists, 84 % had good overall knowledge on the inventory control tools and 59 participants were willing to acquire additional training on inventory control techniques.
Conclusion: The study explained the need for the application of ABC and HML analysis, and the involvement of hospital pharmacists in the regular implementation of inventory analysis towards effective management of Pharmacy stores in a hospital setting.
EFFECT OF GAMMA IRRADIATION ON ANTIOXIDANT POTENTIAL AND BIOACTIVES OF A COSMECEUTICALLY SIGNIFICANT CHLORELLA EMERSONII KJ725233
Authors: Sawant Sneha Sunil , Devi Singh Reema , Ghosh Sukhendu , Kelkar-Mane Varsha 
Published: August 2019

Objective : Gamma radiation induces free radicals with a corresponding alteration in the cell’s antioxidant defense system. The present study thus aimed at determining the role of gamma irradiation in improving the cosmeceutical potential of CEK in terms of antioxidants.
Methods : C.emersonii KJ725233 was subjected to low (100 Gy) and high (1000 Gy) gamma irradiation doses and its effect on the chlorophyll content was evaluated. The quantitative alterations in the antioxidant content of CEK were evaluated by phosphomolybdenum assay (TAC), ferric reducing antioxidant potential (FRAP), 2, 2 - Dipheny-1-picryl hydrazyl radical scavenging assay (DPPH), total phenolic (TPC) and total flavonoid content (TFC). Also, the corresponding qualitative alterations in the bioactives of CEK were determined by GC-HRMS analysis.
Results : A 179.57±2.55% increase in the total chlorophyll content alongwith a 71.76±2.96%, 32.08±2.16%, 11.67±0.89%, 42.85±8.0% and 31.37±3.18% increase was observed in the TAC, FRAP, DPPH radical scavenging, TPC and TFC was observed in CEK irradiated at 1000 Gy. GC-HRMS analysis revealed the induction of Vitamin E on irradiation at both the doses with a corresponding decrease in the phytol content whereas 100 Gy stimulated the induction of phytosterols.
Conclusion : The potent intrinsic antioxidant activity of cosmeceutically significant CEK can be elevated with the induction of the most sought after antioxidant in cosmetology – Vitamin E on irradiation.
OLEOGELS OF OLIVE OIL AND SOYBEAN OIL FOR TOPICAL DRUG DELIVERY: A COMPARATIVE ANALYSIS
Authors: ASH DIPANJANA , MAJEE SUTAPA BISWAS , BISWAS GOPA ROY 
Published: August 2019

Objective: The objective of the present investigation was to develop olive and soybean oil-based oleogels with Span 40 and/or Tween 80 (as gelator and/or surfactant) and determine the critical gelator concentration (CGC), characterise and compare the rheological, thermal properties and drug release profile of the gels formed for topical delivery.
Methods: Olive and soybean oil-based Span 40 and Span 40/Tween 80 oleogel formulations were prepared by solid fiber mechanism and subjected to organoleptic evaluation, FT-IR spectroscopy, thermal analysis, rheological study, kinetic modeling of gelation and drug release.
Results: The critical gelator (Span 40) concentration was found to be lower for olive oil (12% w/v) and depend on the type of oil. Tween 80 reduced CGC of soybean oleogels only. Soybean oil-based oleogel containing 18% w/v Span 40 was found to form more flexible, less viscous and thermally less stable formulation with better release of paracetamol as evident from lower melt flow index, Tg value, lower β and higher α value compared to olive oil-based oleogel with 12% w/v Span 40 (CGC). Surfactant addition can be assumed to modify the microarchitecture of the oleogels to a great extent to produce more flexible and thermally stable gels with even better drug release profile. Span-Tween based soybean oleogel formed a gel-matrix whereas matrix in olive oil-based oleogels containing Span only became slightly flexible to release the drug in zero-order fashion on the addition of surfactant cogelator.
Conclusion: Nature of oil exerts profound influence on the rheological, thermal and release profile of oleogels containing Span 40 as gelator and/or Tween 80 as surfactant cogelator.
Weathering and biodegradation of hydrothermal petroleum in the north rift of Guaymas Basin, Gulf of California
Authors: Simoneit Bernd R.T. , Oros Daniel R. , Leif Roald N. , Medeiros Patricia M. 
Published: August 2019

The Guaymas Basin, Gulf of California, is an actively spreading ocean basin, part of the system of spreading axes and transform faults extending from the East Pacific Rise to the San Andreas fault. Upward migration of hydrothermal petroleum in the basin rifts occurs by both bulk transport and high temperature/pressure aqueous and supercritical gaseous (e.g. CO2, CH4) fluid phases into the seabed sediments. The resulting mounds are laden with the youngest hydrothermal petroleum known to date. The north rift hydrothermal system has been dormant for ~ 3000 years, and organic geochemical analyses of sediment samples collected by both piston and push corers and by dredging operations from various cruises to the Gulf of California show that the organic matter is composed primarily of autochthonous lipids from marine biota, overprinted by hydrothermal petroleum. The chemical composition of this petroleum indicates severe biodegradation for exposed samples, based on the contents of aliphatic, aromatic, steroid, and hopanoid biomarkers. Sulfurized lipids are enriched in the biodegraded oils. Both hydrothermal minerals and petroleum in the mounds of the north rift seabed have been weathered by oxidative/microbial processes.
PENGEMBANGAN MODUL FISIKA BERBASIS MODEL PEMBELAJARAN PROCESS ORIENTED GUIDED INQUIRY LEARNING UNTUK MENINGKATKAN KEMAMPUAN KOGNITIF
Authors: Zamista Adelia Alfama 
Published: August 2019

<p>Kognitif diartikan sebagai potensi intelektual, lebih jelasnya kemampuan kognitif diartikan sebagai kemampuan yang dapat diamati dari aktivitas mental untuk memperoleh pengetahuan melalui pengalaman sendiri. Berdasarkan studi pendahuluan yang dilakukan di STT Dumai diketahui bahwa belum tersedia bahan ajar yang memfasilitasi mahasiswa untuk membangun pengetahuannya sendiri. Maka pada penelitian ini dilakukan pengembangan dan penerapan modul fisika berbasis model pembelajaran <em>process oriented guided inquiry learning </em>(POGIL) untuk meningkatkan kemampuan kognitif mahasiswa. Model pengembangan yang digunakan pada penelitian ini adalah model ADDIE yang terdiri dari tahapan analysis, <em>design, development, implementation, and evaluation. </em>Untuk melihat peningkatan kemampuan kognitif mahasiswa pada tahap <em>implementation </em>model ADDIE dilakukan <em>pre experiment</em> dengan <em>one group</em> <em>pretest-posttest design</em>, dan sampel penelitian dipilih menggunakan metode <em>simple random sampling</em>. Instrumen penelitian yang digunakan untuk menyelidiki peningkatan kemampuan kognitif mahasiswa adalah Â tes. Pengolahan data kemampuan kognitif dilakukan dengan menghitung rata-rata gain yang dinormalisasi. Hasil penelitian menunjukkan bahwa terjadi peningkatakan kemampuan kognitif mahasiswa yang ditunjukkan dengan nilai rata-rata gain yang dinormalisasi sebesar 0,62 pada kategori sedang.</p>
PENGEMBANGAN MANUSIA INDONESIA SEUTUHNYA BERDASARKAN MODEL KAMPUS â€œREHUHâ€ (RELIGIUS, HUMANIS, HARMONIS)
Authors: Yusutria Yusutria , Febriana Rina 
Published: August 2019

<p align="center"><em>Education in Indonesia cannot be separated from the ideals of humanity and justice for all nations, as well as the realization of godly national intelligence, therefore a concept is needed to develop Indonesian people as a whole based on "REHUH" which is a religious, humane and harmonious campus. The research method is library research by analyzing descriptive. Indonesian human development is entirely based on the "REHUH" campus model, based on religion, intellectual, emotional, moral and psychological.</em></p><p><em>The basic concept is with; (1) inviting; (2) provide guidance and lessons; (3) conduct deliberations in a good manner; (4) asking God for guidance. Human development is based on; (1), Support the development of a humanistic culture in developing HR that is creative, innovative and friendly. (2). Instill religious and moral campus culture. (3). The lecturer is a minimum S-2, and has four main competencies, professional, pedagogic, personal and social</em></p><p align="center">Â </p>