bluuebunny/crossref_metadata_2025_split huggingface dataset:
The role of nitrogen gas in fluidized bed reactors on the nodular iron nitridation processs
Authors: Sujana Wayan , Widi Komang Astana , Pohan Gerald A. , Prihatmi Tutut Nani , Ekasari Luh Dina 
Published: March 2021

Abstract
Nitriding process using ammonia gas as a nitrogen source has been carried out with the aim of improving the surface quality of nodular cast iron until the highest surface hardness reaches 426.2 HV. In this study, the nitriding process was carried out with a variety of advanced processes using nitrogen gas in the fluidized bed reactor and without using nitrogen gas in the muffle furnace with a constant variable nitriding time of 4 hours. The continued process is 2 hours each, while the gas composition used is 80% ammonia and 20% nitrogen with a total gas flow of 0.7 m2 hour−1. From the result of SEM test analysis obtained in the advanced process variations with nitrogen gas in the fluidized bed reactor and without nitrogen gas in the muffle furnace, on the surface of nodular cast iron a nitride layer was formed in the form of Fe3N and Fe4N. The largest average thickness formed in advanced process specimens using nitrogen gas in the fluidized bed reactor was 24.64 µm and without using nitrogen gas was 6.06 µm. The EDX test analysis showed that the specimens that received treatment with nitrogen gas had a nitrogen gas content of 43.7% higher than the specimens that did not receive the treatment. The hardness test results showed the distribution of hardness values in specimens that received treatment with nitrogen and without nitrogen. The highest surface hardness is 560.6 HV on the surface of the specimen with nitrogen gas while the specimen without nitrogen gas has a hardness of 426.2 HV.
Injection process parameter analysis of metal injection molding for green part orthopedic implants
Authors: Wiranegara Haruman , Syahputra Donny , Arief Tria Mariz 
Published: March 2021

Abstract
Based on Permenkes RI No. 86 of 2013 the need for orthopedic implants in the jaw / face is increasing. In orthopedics, implants are devices that are placed in place of bone to support fractures. Referring to the statement, Balai Besar Logam dan Mesin (BBLM) conducted research on the manufacture of orthopedic implant products for the face using Metal Injection Molding (MIM) technology. MIM is a combination of the characteristics of Powder Metallurgy and Plastic Injection Molding for the production of complex metal components. The injection process is a major factor that needs to be considered to prevent injection defects including incomplete fill defects in order to proceed to the post-molding process. Based on this, it is necessary to simulate and analyze process parameters to find the maximum process parameters in order to prevent incomplete fill defects. The analysis was carried out using injection molding simulation software, namely Sigmasoft, to the previously obtained design, with a variation setting of the filling temperature value of 170°C-190°C and filling time of 0.5s-2.5s. Variations in values are processed to obtain a combination of parameter settings analyzed. The simulation software generates filling pressure values and filling percentages in the mold cavity. Demonstrate optimal parameters for green part orthopedic implants with Catamold 316L feedstock. The optimal injection operating parameters are at a melting temperature of 180°C, filling time of 1s and filling pressure of 700 bar.
Assimilating region of interest for fused biometric modalities through cie l*a*b* color space
Authors: Bhargavi Devi P. , Sharmila K. 
Published: March 2021

Abstract
Idiosyncrasy in recent times has been a challenge that is not quite easily effectuated in many domains and work structures. Security compromises have led to immeasurable loss and altered the deportment of individuals. In order to counter such circumstances, biometric modality analysis and utilization stimulate an inimitable framework to enhancement of security mitigated impostures. Nonetheless, the existing indagation has always instituted the single biometric modality processing and evaluation. Although the previous work of pre-processing and post-processing for biometric modalities such as fingerprint, iris, gait, and other physiological and behavioral modalities have been considered through explicit algorithmic and computational processing, the rate of precision fallacy and contriving unique identification has been a lingering question. This paper focuses on augmenting the efficacy of identification through the fused biometric modalities of fingerprint and iris, along with unsheathing the region of interest (ROI) by designating color masks using the CIE L*a*b* color space and delta disparity computation. The histogram analysis is implemented to analyze the pixel intensity, and the pixel matched extraction is surface plotted using MATLAB.
CICERONE- A Real Time Object Detection for Visually Impaired People
Authors: Mahesh Therese Yamuna , Parvathy S S , Thomas Shibin , Thomas Shilpa Rachel , Sebastian Thomas 
Published: March 2021

Abstract
Our work provides a solution to people who are suffering from partial blindness due to diseases of the eye or due to accidents. Unlike people who are born blind, they depend a lot on other people for doing their day to day activities. Our project is a boon to such people as our end product; a smart walking stick detects the trained objects that are used daily by the person and intimates the person using audio messages. This project changes the visual world into an audio world by informing the visually impaired of the objects in their environment. These people can use this particular prototype for self -navigating their way. A YOLO (You look only once) algorithm is used in our project. Real-time objects in an image are detected with their names represented on a bounding box and these names are converted to speech signals. The conversion to audio signals is done by using an e-Speak tool which forms Googles Text to Speech (gTTS) system. The prototype consists of several modules. The Raspberry Pi camera module takes the image and transfers it to the Raspberry Pi desktop. Then, real-time object detection is carried out by using YOLO network. The detected image is converted to speech by using the gTTS module and the audio result is provided to the user through a headset. For achieving the required portability, the battery backup is being used.
Performance analysis of Word Embeddings for Cyberbullying Detection
Authors: Pericherla Subbaraju , Ilavarasan E 
Published: March 2021

Abstract
Cyber bullying activities are increasing day by day with the increase of Social Media Platforms such as Face book, Twitter, Instagram etc. Bullies take the advantage of these large online connected platforms due to which it became as a big challenging task in Natural Language Processing (NLP). In this paper, we compare the performance of various word embedding methods from basic word embedding methods to recent advanced language models such as RoBERTa, XLNET, ALBERT, etc. for cyberbullying detection. We used LightGBM and Logistic regression classifiers for the classification of bullying and non-bullying tweets. Among all the models, RoBERTa is outperformed as compared to state-of-the-art models.
Automatic Detection of Anomalies in Video Surveillance using Artificial Intelligence
Authors: Krishnan Sreedevi R , Amudha P , Sivakumari S 
Published: March 2021

Abstract
The significance of security in day to day life is increasing, and hence the use of a video surveillance system is becoming commonly accepted in almost every public places. Even though by placing surveillance system in public places will help to trace out the culprits of the anomalous situation, it is hard to trace it out. The knowledge of the incidence time is needed to identify the event, and also, it requires an enormous data handling task. To identify the anomalous situation in real-time will help the authorities to decrease the consequences and loss during the anomalous event. The paper proposes an in-depth study of various automatic anomaly detection techniques which helps to reduce the loss occurred of the anomalous situation. The advancements in Artificial Intelligence help in the quick and automatic identification of nominal and anomalous events. The sequential and incremental learning approach in feature extraction will help to generate a model that will provide much accurate classifications and predictions of anomalies.
Heart disease prediction system using Correlation Based Feature Selection with Multilayer Perceptron approach.
Authors: Kuruvilla Ambily Merlin , Balaji N.V 
Published: March 2021

Abstract
Cardiac disease prediction helps physicians to make accurate recommendations on the treatment of the patients. The use of machine learning (ML) is one of the solution for recognising heart disease-related symptoms. The goal of this study is to suggest a methodology for identifying the most relevant features of cardiac disease characteristics by applying a feature selection technique. The data set used in this study was Framingham heart disease dataset (FHS). It was collected from KAGGLE Machine Learning repository. There are 16 attributes and a mark in the dataset that has been validated by four ML classifiers. There are two feature selection methods, Correlation Based Feature selection (CBFS) and Principle Component Analysis (PCA) was used for the comparison in the study. By using CBFS Method five highly correlated features are selected for the study, and by using PCA thirteen features are selected. The experimental result shows that Correlation Based Feature Selection with Multilayer perceptron (CBFS with MLP) obtained the highest accuracy for this dataset.
Empirical Statistical Analysis and Cluster Studies on Socio-Economic Status (SES) Dataset
Authors: Balasankar V. , Varma Penumatsa Suresh Suresh , Pandu Ranga Vital T. 
Published: March 2021

Abstract
Socio-economic status (SES) levels and conditions are extremely influential variables in the study of a particular area of society or any society. Social factors, for instance, the position of caste, religion, marital status, education levels, give good assessment results for us about a person’s goals and the method of achieving their objectives. Generally economic status of any family is needy upon the social factors, for instance, the size of the family, educators in family and levels, and the level of the friendly environment in the family. SES with machine learning (ML) especially cluster analysis is important to identify important features or dimensions of the SES dataset, evaluate the rakings of dimensions and dimensional reductions. In this research, we collected 1742 samples (household information) as per socio-economic ratios and area (rural and urban) wise ratios with good questionnaires between 2018 and 2019 from Rajamahandravaram, East Godavari District, AP, India. We conduct the statistical analysis and cluster analysis for identifying the important factors of SES levels and their problem analysis. In cluster analysis, we apply k-means, hierarchal clustering (HC), and hierarchal with principal component analysis (PCA). The good projection results related to HC and PCA-HC specifies passements of SES class values.
Real Time Resource Scheduling Framework for Smart City
Authors: Talmale Girish , Shrawankar Urmila 
Published: March 2021

Abstract
Smart City plays a vital role in social development. Real time scheduling of different tangible and intangible resource in distributed environment in smart is challenging problem. The different disaster management tasks smart city applications such as floods, earthquake, pandemic, fire, disaster relief, and control of hazardous area, major accidents, control of terrorism and other attacks require real time emergency resources scheduling. The existing techniques uses for smart city resource management are suffering from low resource utilization and high response time, migration and preemption overhead. This work paper presents cluster based real time resource scheduling framework for emergency situation in smart city. The edge computing resources are group into cluster. The proposed framework increases resources utilization and reduces response time, migration, preemption overheads. The emergency tasks are scheduled to these computing cluster are allow to migrate within the cluster to ensure to reduce migration, switching overhead, response time and increase resource utilization and success rate.
Engineering the early bone metastatic niche through human vascularized immuno bone minitissues
Authors: Colombo Maria Vittoria , Bersini Simone , Arrigoni Chiara , Gilardi Mara , Sansoni Veronica , Ragni Enrico , Candiani Gabriele , Lombardi Giovanni , Moretti Matteo 
Published: March 2021

Abstract
Bone metastases occur in 65%–80% advanced breast cancer patients. Although significant progresses have been made in understanding the biological mechanisms driving the bone metastatic cascade, traditional 2D in vitro models and animal studies are not effectively reproducing breast cancer cells (CCs) interactions with the bone microenvironment and suffer from species-specific differences, respectively. Moreover, simplified in vitro models cannot realistically estimate drug anti-tumoral properties and side effects, hence leading to pre-clinical testing frequent failures. To solve this issue, a 3D metastatic bone minitissue (MBm) is designed with embedded human osteoblasts, osteoclasts, bone-resident macrophages, endothelial cells and breast CCs. This minitissue recapitulates key features of the bone metastatic niche, including the alteration of macrophage polarization and microvascular architecture, along with the induction of CC micrometastases and osteomimicry. The minitissue reflects breast CC organ-specific metastatization to bone compared to a muscle minitissue. Finally, two FDA approved drugs, doxorubicin and rapamycin, have been tested showing that the dose required to impair CC growth is significantly higher in the MBm compared to a simpler CC monoculture minitissue. The MBm allows the investigation of metastasis key biological features and represents a reliable tool to better predict drug effects on the metastatic bone microenvironment.
Stable Legendre–Lorentzian solitons in localized optical potentials
Authors: Fernandez-de la Garza J A , López-Aguayo S 
Published: March 2021

Abstract
We derive a family of optical solitons from a non-linear Schrödinger’s equation with an external potential in a (1 + 1)D system. The soliton solutions can be expressed in a closed form by using a Lorentzian apodization and associated Legendre functions, in contrast to the more common solitons using a Gaussian apodization. Similarly, an analytical and bounded form for the external optical potential is also found, and furthermore, a general formula for the power is obtained. Remarkably, we found that these solitons are stable in their propagation for certain values of power and width of the solitons. Finally, we report several interesting propagation dynamics for the unstable scenario: from loss of the beam’s inner structure by splitting of the initial soliton profile to breathing decaying solitons.
Mamalu Poepoe: Enhancing Hawaii’s Biosecurity Through Interagency Collaboration
Authors: Kaufman Leyla V 
Published: March 2021

Abstract
Hawaii’s role as a hub for travel, commerce, and military transport is a major contributor to its vulnerability to invasive species incursions. Ports of entry, airports, and seaports, besides being important invasive species pathways are also sites that present the first opportunity for early detection and rapid response. Mamalu Poepoe is an interagency program to enhance invasive species detection at Hawaii’s main airports. The program brings together multiple state agencies including the Hawaii Departments of Transportation, Agriculture, Health, Land and Natural Resources, and the University of Hawaii. The program also collaborates with several projects under the University of Hawaii system such as the Hawaii Ant Lab and the Invasive Species Committees statewide. Priority target species in the program include: mosquitoes, the coconut rhinoceros beetle (CRB: Oryctes rhinoceros Linnaeus), Africanized honeybees (AHB: Apis mellifera scutellate Lepeletier), and invasive ants. Mamalu Poepoe fills an important gap in surveillance at Hawaii’s airports and enhances the state’s ability for early detection and rapid response of species that can severely affect human health, our environment, and the economy through collaborative efforts.
Diaspora Voting In Kenya: A Promise Denied
Authors: Wellman Elizabeth Iams , Whitaker Beth Elise 
Published: March 2021

Abstract
In 2010, Kenya extended voting rights to its estimated 3,000,000 citizens living abroad, thus joining a growing number of countries in Africa and around the world to recognize emigrant voting rights. Yet despite a politically engaged diaspora, intensive government outreach to emigrants, and high-stakes electoral competition, fewer than 3,000 Kenyans were permitted to vote from abroad in the 2013 and 2017 presidential elections. What explains the failure of the Kenyan government to implement diaspora voting on a broader scale? Drawing on original interviews and archival documents, this article analyses the tumultuous battle over the adoption and implementation of external voting in Kenya, focusing especially on legal, logistical, and political challenges. We argue that uncertainty about the number of Kenyan emigrants and their political preferences, paired with a highly competitive electoral climate, meant there was little political will to push for more widespread implementation of diaspora voting. Our analysis of external voting in Kenya has implications for diaspora participation in other competitive electoral contexts across the continent and beyond.
LILRB1: A Novel Diagnostic B-Cell Marker to Distinguish Neoplastic B Lymphoblasts From Hematogones
Authors: Saumell Tutusaus Silvia , Pirruccello Elaina , Fuda Franklin , Churchill Hywyn , Chen Dong , Zhang Cheng Cheng , Chen Weina 
Published: March 2021

Abstract

Objectives
New B-cell markers are needed for monitoring B lymphoblastic leukemia (B-ALL) in the era of immunotherapies directed against CD19 and CD22. The expression of leukocyte immunoglobulin-like receptor subfamily B member 1 (LILRB1) on hematogones in bone marrow (BM) and neoplastic B lymphoblasts has not yet been systematically investigated.

Methods
We assessed LILRB1 expression pattern on B cells in 19 control BMs and 22 B-ALL cases by flow cytometry.

Results
In all cases, mature B cells and hematogones exhibited a consistent pattern of LILRB1 expression with variable intensity over different stages of maturation, including a characteristic V-shaped pattern on hematogones. While neoplastic B lymphoblasts in all cases expressed LILRB1, the pattern of expression was distinctly abnormal relative to hematogones (loss of the dynamic pattern in all cases and abnormal expression levels in 83% of cases).

Conclusions
LILRB1 is a novel diagnostic B-cell marker to aid in distinguishing neoplastic B lymphoblasts from hematogones.
The neuroimaging evidence of brain abnormalities in functional movement disorders
Authors: Sasikumar Sanskriti , Strafella Antonio P 
Published: March 2021

AbstractNeuroimaging has been pivotal in identifying and reframing our understanding of functional movement disorders. If accessible, it compensates for the limitations of the clinical exam and is especially useful where there is overlap of functional symptoms with classical presentations of disease. Imaging in functional movement disorders has increasingly identified structural and functional abnormalities that implicate hypoactivation of the cortical and subcortical motor pathways and increased modulation by the limbic system. Neurobiological theories suggest an impaired sense of agency, faulty top-down regulation of motor movement and abnormal emotional processing in these individuals. This framework challenges our traditional understanding of functional movement disorders as distinct from the deceptive term of ‘organic’ diseases and proposes that these conditions are not considered as mutually exclusive. This update summarizes the literature to date and explores the role of imaging in the diagnosis of functional movement disorders and in detecting its underlying molecular network.
Genetic analysis of Pycr1 and Pycr2 in mice
Authors: Stum Morgane G , Tadenev Abigail L D , Seburn Kevin L , Miers Kathy E , Poon Pak P , McMaster Christopher R , Robinson Carolyn , Kane Coleen , Silva Kathleen A , Cliften Paul F , Sundberg John P , Reinholdt Laura G , John Simon W M , Burgess Robert W 
Published: March 2021

Abstract
The final step in proline biosynthesis is catalyzed by three pyrroline-5-carboxylate reductases, PYCR1, PYCR2, and PYCR3, which convert pyrroline-5-carboxylate (P5C) to proline. Mutations in human PYCR1 and ALDH18A1 (P5C Synthetase) cause Cutis Laxa (CL), whereas mutations in PYCR2 cause hypomyelinating leukodystrophy 10 (HLD10). Here, we investigated the genetics of Pycr1 and Pycr2 in mice. A null allele of Pycr1 did not show integument or CL-related phenotypes. We also studied a novel chemically-induced mutation in Pycr2. Mice with recessive loss-of-function mutations in Pycr2 showed phenotypes consistent with neurological and neuromuscular disorders, including weight loss, kyphosis, and hind-limb clasping. The peripheral nervous system was largely unaffected, with only mild axonal atrophy in peripheral nerves. A severe loss of subcutaneous fat in Pycr2 mutant mice is reminiscent of a CL-like phenotype, but primary features such as elastin abnormalities were not observed. Aged Pycr2 mutant mice had reduced white blood cell counts and altered lipid metabolism, suggesting a generalized metabolic disorder. PYCR1 and -2 have similar enzymatic and cellular activities, and consistent with previous studies, both were localized in the mitochondria in fibroblasts. Both PYCR1 and -2 were able to complement the loss of Pro3, the yeast enzyme that converts P5C to proline, confirming their activity as P5C reductases. In mice, Pycr1; Pycr2 double mutants were sub-viable and unhealthy compared to either single mutant, indicating the genes are largely functionally redundant. Proline levels were not reduced, and precursors were not increased in serum from Pycr2 mutant mice or in lysates from skin fibroblast cultures, but placing Pycr2 mutant mice on a proline-free diet worsened the phenotype. Thus, Pycr1 and -2 have redundant functions in proline biosynthesis, and their loss makes proline a semi-essential amino acid. These findings have implications for understanding the genetics of CL and HLD10, and for modeling these disorders in mice.