bluuebunny/crossref_metadata_2025 huggingface dataset:
Customer churn analysis using XGBoosted decision trees
Authors: Vasudevan Muthupriya , Narayanan Revathi Sathya , Nakeeb Sabiyath Fatima , Abhishek Abhishek
Published: January 2022

Customer relationship management (CRM) is an important element in all forms of industry. This process involves ensuring that the customers of a business are satisfied with the product or services that they are paying for. Since most businesses collect and store large volumes of data about their customers; it is easy for the data analysts to use that data and perform predictive analysis. One aspect of this includes customer retention and customer churn. Customer churn is defined as the concept of understanding whether or not a customer of the company will stop using the product or service in future. In this paper a supervised machine learning algorithm has been implemented using Python to perform customer churn analysis on a given data-set of Telco, a mobile telecommunication company. This is achieved by building a decision tree model based on historical data provided by the company on the platform of Kaggle. This report also investigates the utility of extreme gradient boosting (XGBoost) library in the gradient boosting framework (XGB) of Python for its portable and flexible functionality which can be used to solve many data science related problems highly efficiently. The implementation result shows the accuracy is comparatively improved in XGBoost than other learning models.
Fixed point theorem between cone metric space and quasi-cone metric space
Authors: Al-Yaari Abdullah , Sakidin Hamzah , Alyousifi Yousif , Al-Tashi Qasem
Published: January 2022

This study involves new notions of continuity of mapping between quasi-cone metrics spaces (QCMSs), cone metric spaces (CMSs), and vice versa. The relation between all notions of continuity were thoroughly studied and supported with the help of examples. In addition, these new continuities were compared with various types of continuities of mapping between two QCMSs. The continuity types are 𝒇𝒇-continuous, 𝒃𝒃-continuous, 𝒇𝒃-continuous, and 𝒃𝒇-continuous. The results demonstrated that the new notions of continuity could be generalized to the continuity of mapping between two QCMSs. It also showed a fixed point for this continuity map between a complete Hausdorff CMS and QCMS. Overall, this study supports recent research results.
Max stable set problem to found the initial centroids in clustering problem
Authors: Karim Awatif , Loqman Chakir , Hami Youssef , Boumhidi Jaouad
Published: January 2022

In this paper, we propose a new approach to solve the document-clustering using the K-Means algorithm. The latter is sensitive to the random selection of the k cluster centroids in the initialization phase. To evaluate the quality of K-Means clustering we propose to model the text document clustering problem as the max stable set problem (MSSP) and use continuous Hopfield network to solve the MSSP problem to have initial centroids. The idea is inspired by the fact that MSSP and clustering share the same principle, MSSP consists to find the largest set of nodes completely disconnected in a graph, and in clustering, all objects are divided into disjoint clusters. Simulation results demonstrate that the proposed K-Means improved by MSSP (KM_MSSP) is efficient of large data sets, is much optimized in terms of time, and provides better quality of clustering than other methods.
A deep learning approach based on stochastic gradient descent and least absolute shrinkage and selection operator for identifying diabetic retinopathy
Authors: Ramanathan Thirumalaimuthu Thirumalaiappan , Hossen Md. Jakir , Sayeed Md. Shohel , Raja Joseph Emerson
Published: January 2022

More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
Radial force cancellation of bearingless brushless direct current motor using integrated winding configuration
Authors: Yousif Ali A. , Mohammed Ahmed M. , Ali Mohammed Moanes E.
Published: January 2022

A bearingless brushless direct current (BLDC) motor incorporates the function of magnetic bearings into a BLDC motor, making it a new type of high-performance motor. In this paper, the main motor windings are used to generate the radial force cancellation by injecting the required dc current, “integrated winding configuration”. The bearingless BLDC motor, direct current (DC) cancellation system model is established with the aid of (ANSYS/MAXWELL) software. The simulation results confirm that the rotor radial force is approximately zero and results from a balanced distribution of the magnetic flux density. The proposed DC excitation system is suitable to realize the rotor radial force cancellation in the bearingless BLDC motor. The simulation results of the proposed configuration show the approach of integrating winding configuration at different active pole positions to find the more efficient suspension performance and reduce the suspensions system current.
Antimicrobial Activity of Stenochlaena palustris and Sauropus androgynus in Staphylococcus aureus, Escherichia coli and Candidia albicans
Authors: Budiarti Lia Yulia , Isnaini Isnaini , Dayana Puteri , Sari Norma , S. Nur Almira R.
Published: January 2022

Stenochlaena palustris and Sauropus androgynus are known to contains antimicrobial substances such as flavonoids, saponins and tannins compounds.The purpose of this study was to analyzes the antimicrobial activity of young and old leaf infusions of S. palustris and S. androgynus leaves against Staphylococcus aureus, Escherichia coli and Candida albicans. Analyze the antibacterial activity of a single preparations with a combination preparation of S.palustris (SP) and S.androgynus (SA) leaves infusion against S.aureus, E.coli and C.albicans. Leaves of S.palustris young part (SP1) taken 0-10 cm from shoots and old parts (SP2) 11-20 cm from shoots, while leaves of S.androgynus young part (SA1) leaves number 1 - 10 from the top and the old part (SA2) leaves number 11-20 from the top. The results showed that a single infusion of SP1 75% and SP2 75%, SA1 90% and SA2 90%, and a combination of SP1 75% and SA1 75%, SP2 75% and SA2 75% have the same activity as ampicillin in S.aureus. Single infusion of SP1 90% and SP2 90%, SA1 90% and SA2 90%, combination of SP1 75% and SA1 80% and the combination of SP2 80% and SA2 60% have the same activity as ciprofloxacin in E. coli. Single infusion of SP1 90% and SP2 90%, and a combination of SP1 80% and SA1 80%, SP2 80% and SA2 80% have the same activity as ketoconazole in C.albicans. The difference in activity due to differences in leaf parts used only occurred in E. coli, whereas in S.aureus and C.albicans (p <0.05).
Understanding Natural Regeneration in Burned Tropical Peatland: A Strategy to Accelerate the Forest Recovery Process
Authors: Lestari Dwi Puji , Darusman Taryono , Harsanto Fransiskus , Arriyadi Desra , Ginanjar Ginanjar
Published: January 2022

The 2015â€™s massive forest fires have left large areas of burned peatlands that need to be restored, demanding substantial number of resources. To understand natural regeneration on burned peatland and how planting might accelerate its recovery process, we measured recruitment on burned peatland with different fire frequency. Three transects were established each consisting of five 20x20 m2plots developed at a 30 m interval. Our finding suggests that the absence of remnant trees due to frequent or severe fires does not always impede the emergence of new recruitments, although diversity of forest regrowth is likely to be affected by its proximity to forest remnant. The floristic composition also showed a domination of pioneer species, giving evidence that forest recovery is initiated. We conclude that to support the recovery process through planting activity, the successional stage of the designated sites should be determined first. During the early phase of succession, stand initiation should be addressed first by the colonization of rapidly recruiting species to ensure continuous regeneration. Therefore, the common planting method on large areas with mixed climax-high valuable trees is unbeneficial unless the restoration sites have reached the later stages of succession.
Growth Inhibition of Fungal Plant Pathogens by Antagonist Bacteria Using Dual Culture Assays
Authors: Ramona Yan -
Published: January 2022

There has been excessive use of chemical-based pesticides globally resulting in significant environmental consequences with adverse effects on human health. Therefore more sustainable environment friendly alternative solutions are needed to cope with such environmental and health challenges. Development of biocontrol method is a rational solution to these challenges and therefore the main objective of this research were to isolate and screen potential antagonist bacteria inhibitory to important fungal plant pathogens (Fusarium oxysporum, Ceratocytis sp., Aspergillus flavus dan Aspergillus niger) in vitro by applying dual culture assays on potato dextrose agar (PDA) or trypticase soya agar (TSA) media. The antagonist bacteria were isolated from various sources such as soil of rhizosphere zone and roots of lettuce plants and Â mature compost. The potential of antagonist candidates were screened on the basis of inhibitory activity against targeted fungal pathogens. Bacterial antagonists with highest zone of inhibition were identified up to genus level using biochemical tests, and the results were matched with those specified in the Bergey’s Manual of determinative bacteriology. Fifteen bacterial isolates were successfully isolated from various sources, and 60% of those isolates showed antagonistic activity in vitro against fungal pathogens with various degree of inhibition.Â  This indicated the initial potential to develop as biocontrol agents. Based on preliminary identification, genera of Bacillus and Pseudomonas were found to be the predominant isolates and in addition genus Acinetobacter was also identified in this study.
Qualidade do sono de escolares saudáveis: registro de cinco casos
Authors: Itaborahy Bianca Dana Horongozo , Castilho Tayná , Ducati Gabriela Castilhos , Schivinski Camila Isabel Santos
Published: January 2022

Introdução: a qualidade do sono é um aspecto fundamental do desenvolvimento infantil, pois quando prejudicada, pode ter como consequência eventos patológicos que acometem a qualidade de vida e o desenvolvimento da criança. Objetivo: apresentar o registro de dados da poligrafia durante o sono de um grupo de escolares hígidos. Metodologia: relatou-se o registro de cinco casos de escolares saudáveis que foram avaliados por meio de poligrafia noturna domiciliar (PND) e o questionário Sleep Behaviour Questionaire (SBQ). Também foi realizada antropometria, espirometria e aplicado o questionário International Study of Asthma and Allergies in Childhood (ISAAC). Resultados: A idade média dos casos analisados foi de 10,8±1,78 anos (1 menino) e o índice de massa corporal (IMC) 18,94±2,45 cm/m2. O tempo médio de registro na PND foi de 430 minutos (7,1 horas), com média de saturação de pulso de oxigênio de 95,8%, e frequência cardíaca de 64,3 bpm. O índice de apneia e hipopneia (IAH) variou entre 2 e 4,3 por hora (média de 2,9/hora) e, quando analisados somente os eventos na posição supina, a média do IAH foi de 3,38/hora. Todas as crianças apresentaram ronco noturno, com grande variação entre elas (4-63 episódios de roncos/ noite) e o SBQ pontuou valor sugestivo de distúrbio respiratório do sono (DRS) de caráter leve (média de 42,8 pontos). Conclusão: os escolares hígidos analisados manifestaram sintomas e indicativos de DRS, como apneia do sono leve, segundo os instrumentos utilizados.
Horos
Authors: Potter Thea
Published: January 2022

In Horos, Thea Potter explores the complex relationship between classical philosophy and the ‘horos’, a stone that Athenians erected to mark the boundaries of their marketplace, their gravestones, their roads and their private property.Potter weaves this history into a meditation on the ancient philosophical concept of horos, the foundational project of determination and definition, arguing that it is central to the development of classical philosophy and the marketplace.

Horos challenges many significant interpretations of ancient thought. With nuance and insight, Potter combines the works of Aristotle, Plato, Homer and archaic Greek inscriptions with the twentieth-century continental philosophy of Heidegger, Derrida and Walter Benjamin. The result is a powerful study of the theme of boundaries in classical Athenian society as evidenced by boundary stones, law and exchange, ontology, insurgency and occupation.

The innovative book will be of interest to scholars in the fields of ancient Greek social history, philosophy, and literature, as well as to the general reader who is curious to know more about classical life and philosophy.
1. A New Ancient Petrography
Authors: Potter Thea
Published: January 2022

Chapter One (‘A New Ancient Petrography’) provides an overview of the Ancient Greek word horos as it appears in the archaeological record and textual tradition. Given that the definition of its verbal cognate is ‘to determine, divide, define,’ it is suggested that this division is in the heart of language itself. Boundary markers must be read or interpreted as such, implying that the boundary is not a reductively material thing but is something dependent upon us, inside of us. Whatever it was that led us to create boundaries—to make distinctions—also bound us to our linguistic distinctions. This is what a materialist disposition would describe us as: the inscribers, the plinth-builders. The horos, at once stone and term, raises the problem of the boundary between nature and human, between worked stone and natural stone. This problem comes down to us in our distinctions of the physical world. In the absence of a demiurge, matter is supposed to be without meaning, and this is the basis for scientific rationalism. However, even the distinction between meaning and matter relies upon a conceptual acceptance that the boundary between the two is in some way naturally given. This chapter raises the problem of such distinctions and claims that any attempt to define humans as separate to everything else always ends up back at the coincidence of word and stone.
2. Does the Letter Matter?
Authors: Potter Thea
Published: January 2022

Chapter Two (‘Does the Letter Matter?’), taking the definition ‘boundary, landmark[…]pillar (whether inscribed or not)’ as its starting point, returns to the earliest examples of the horos in the archaeological record. Here I confront the Derridean problem of writing as origin. Even if the stone was not marked with the word for boundary (horos), it does not cease to be a boundary because it was nonetheless read as a boundary. Therefore, I turn to the lexicons to discover how the Greeks themselves defined the horos. The result is twofold, like the boundary; a definition of the word must accept the horos as the boundary of writing and reading. It is always inferred in any act of reading because there must be something, whether the inscribed word or the natural rock, for us to read. Horos proliferates from the rock into our definitions of what words mean, and it always remains as the solid foundation of these works of ‘definition.’ It is the difference and bond that is co-terminal with language as such but does not for all that lose its base materiality as stone.
3. Breaking the Law
Authors: Potter Thea
Published: January 2022

Chapter Three (‘Breaking the Law’) considers the legal implications of the horos, taking the meaning ‘bounds, boundaries.’ The regions that are thus separated are given definition by the boundary and exist as different spaces on account of the boundary but also share something in common: the boundary itself. I return to earlier examples of the boundary-stone in the Hebraic and Greek Biblical tradition, where variations of the horos appear repeatedly and ask the question as to why boundary-stones in the Old Testament required the double enforcement both as stone placed upon the land and as prohibition in the written text. The problem of legality is raised and followed into the work of Plato’s Laws, where the first law is given as the prohibition against the removal of the boundary-stone. In these textual traditions, the prohibition that is to follow upon the horos implies that something has been lost from the base materiality, the bare presence of the stone, and this loss is exactly what supports the force of law. The final knife-twist in the letter of the law is described by a leap into the ephebic military service performed upon the boundaries of Attica, where failure to swear allegiance to the horoi meant exile from the Athenian city and its institutions.
7. Solon’s Petromorphic Biopolitics
Authors: Potter Thea
Published: January 2022

The final chapter (‘Solon’s Petromorphic Biopolitics’) resolves the former discussions on the horos by looking at one last meaning, ‘decision of a magistrate.’ The law-reformer Solon is famous for an act called the seisachtheia, where he was said to have relieved the earth from her burdens and freed men who were enslaved. The burdens he claims to have raised were none other than horos-stones. With the reforms of Solon, the web of meanings that the horos seems to have bound begins to unravel, and yet the word itself does not lose its multiplicity. Solon brings an end to a period of civil war and inaugurates an epoch that ensured the productivity of its citizens, limited their ease of movement, and opened the way to the eventual dominance of the market and its persuasive reasoning. He did so by claiming for himself the middle position: in his own words he stood as a horos in the midst of the people. I argue that this created a fracture in the traditions of Athens, disrupting the household and the place of women and their command over reproduction and production, generating, in contrast, a society based upon a centralised political economy. The novelty of this claim is in the idea of biological productivity as a regulative device within Athenian legal discourse.
Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-Convolutional Neural Networks Classification
Authors: Deepa P. V. , Joseph Jawhar S. , Merry Geisa J.
Published: January 2022

The field of nanotechnology has lately acquired prominence according to the raised level of correct identification and performance in the patients using Computer-Aided Diagnosis (CAD). Nano-scale imaging model enables for a high level of precision and accuracy in determining if a brain
tumour is malignant or benign. This contributes to people with brain tumours having a better standard of living. In this study, We present a revolutionary Semantic nano-segmentation methodology for the nanoscale classification of brain tumours. The suggested Advanced-Convolutional Neural Networks-based
Semantic Nano-segmentation will aid radiologists in detecting brain tumours even when lesions are minor. ResNet-50 was employed in the suggested Advanced-Convolutional Neural Networks (A-CNN) approach. The tumour image is partitioned using Semantic Nano-segmentation, that has averaged dice
and SSIM values of 0.9704 and 0.2133, correspondingly. The input is a nano-image, and the tumour image is segmented using Semantic Nano-segmentation, which has averaged dice and SSIM values of 0.9704 and 0.2133, respectively. The suggested Semantic nano segments achieves 93.2 percent and 92.7
percent accuracy for benign and malignant tumour pictures, correspondingly. For malignant or benign pictures, The accuracy of the A-CNN methodology of correct segmentation is 99.57 percent and 95.7 percent, respectively. This unique nano-method is designed to detect tumour areas in nanometers
(nm) and hence accurately assess the illness. The suggested technique’s closeness to with regard to True Positive values, the ROC curve implies that it outperforms earlier approaches. A comparison analysis is conducted on ResNet-50 using testing and training data at rates of 90%–10%,
80%–20%, and 70%–30%, corresponding, indicating the utility of the suggested work.
Early Prediction of Parkinson’s Disease from Brain MRI Images Using Convolutional Neural Network
Authors: Prema Arokia Mary G. , Suganthi N. , Hema M. S.
Published: January 2022

The early diagnosis of Parkinson’s Disease (PD) is a challenging practice for doctors. Currently, there are no separate diagnostics and tests to be done to predict onset PD. However, the PD can be predicted through repeated clinical trials and tests. Sometimes, early prediction
of PD can become tedious based on trials and tests. The computer-aided prediction will help medical professionals predict PD accurately during one’s onset stages to improve the PD patients’ quality of life. Hence, early prediction of PD is essential. In this article, Convolution
Neural Networks (CNN) is proposed to classify PD patients and healthy individuals. The brain MRI images are given as input for the proposed methodology. The CNN deep neural network will first extract the features from the images. Then, it will classify the PD patients and healthy individuals
from the extracted features. The automatic feature extraction will improve the accuracy of the classifier and reduce human error. The brain MRI images are taken from the PPMI dataset for experimentation. The sensitivity, specificity, and accuracy are calculated to assess the performance of
the proposed methodology. The loss is also calculated to verify the performance of the classifier. It is observed that the CNN classifier has produced a higher accuracy of more than 98% in classifying PD patients and healthy individuals when compared to multi-layer perceptron deep learning.
Comparative Analysis of Deep Convolutional Neural Network Models for Humerus Bone Fracture Detection
Authors: Sasidhar A. , Thanabal M. S.
Published: January 2022

Deep learning plays a key role in medical image processing. One of the applications of deep learning models in this domain is bone fracture detection from X-ray images. Convolutional neural network and its variants are used in wide range of medical image processing applications. MURA
Dataset is commonly used in various studies that detect bone fractures and this work also uses that dataset, in specific the Humerus bone radiograph images. The humerus dataset in the MURA dataset contains both images with fracture and without fracture. The image with fracture includes images
with metals which are removed in this work. Experimental analysis was made with two variants of convolutional neural network, DenseNet169 Model and the VGG Model. In case of the DenseNet169 model, a model with the pre trained weights of ImageNet and one without it is experimented. Results
obtained with these variants of CNN are comparedand it shows that DenseNet169 model that uses pre-trained weights of ImageNet model performs better than the other two models.
Anonymization Based on Improved Bucketization (AIB): A Privacy-Preserving Data Publishing Technique for Improving Data Utility in Healthcare Data
Authors: Indhumathi R. , Sathiya Devi S.
Published: January 2022

Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage
of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy
Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining
privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against
privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a
result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.
Efficient and Secure Remote Health Management in Cloud in Vehicular Adhoc Network Environment
Authors: Mohanaprakash K. , GunaSekar T.
Published: January 2022

Vehicle Ad Hoc Networks (VANETs) is a crucial communications framework for transferring messages between any healthcare systems. The dilemma of fixing the safest efficient route is a tedious issue in VANET. Hence the secure and most reliable way will give the appropriate solution for
the routing issues in the VANET. In this paper, by using the Multi-Objective Bio-inspired Heuristic Cuckoo Search Node optimization algorithm is designed to find the efficient safest route for transferring health data within a short period. After seeing the efficient route, the node can be
distinguished upon the traffic and security by using the Stochastic Discriminant Random Forest Node Classifier. Then in the selected route, the nodal distance can be calculated by applying the delay-based weighted end-to-end approach for traffic analysis. Then the authentic vehicle node can
be analyzed through the Trust Aware extreme Gradient Boosting Node Classification based Secured Routing (TAXGBNC-SR) Technique. The obtained information that can be stored in the cloud. It deal with the multiple number of tasks gives to the ARM micro-controllers in order to perform the multiple
tasks that gets logged in the cloud via Internet of Things technology (Iot).
Brain Tumor Detection with Biologically Inspired Watershed Segmentation and Classification Based on Feed-Forward Neural Network (FNN)
Authors: Gopika G. S. , Shanthini J. , Kavitha M. S. , Sabitha R.
Published: January 2022

Image segmentation plays a very vital role in gathering information by dividing the images into various segments to achieve the meaningful information, whereas the image segmentation gives importance in the area of medical imaging to analyze and process the anatomical structures of
various internal organs of the body with high resolution images that are captured during medical examination. Medical experts will go through the reports which give the various reasons for the existence of the disease. Brain which is considered the important part of the body so the detection
and the segmentation of brain tumors will be considered as the major task of the medical field whereas they are using the high resolution images in the form of MRI reports. The MRI images are considered as the vital source for the identification of tumors in the brain. The accuracy of the
segmentation and identification of the tumor depends upon the experience of the radiologist and also it is time consuming task. Therefore the watershed segmentation is performed for the extraction of the tumor region and the features are extracted for the classification, whereas the classification
is carried out by the Feed-Forward Neural Network (FNN). The experimental results are evaluated based on the performance and the quality analysis, Furthermore the results give the accuracy of 91.2% in the training model and 71.8% as the testing during the classification process.
Deep Learning Model for Epileptic Seizure Prediction
Authors: Ganapriya K. , Uma Maheswari N. , Venkatesh R.
Published: January 2022

Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, recurrent neural network (RNN), is designed for predicting the upcoming values in the EEG values. A deep data analysis is made to find the
parameter that could best differentiate the normal values and seizure values. Next a recurrent neural network model is built for predicting the values earlier. Four different variants of recurrent neural networks are designed in terms of number of time stamps and the number of LSTM layers
and the best model is identified. The best identified RNN model is used for predicting the values. The performance of the model is evaluated in terms of explained variance score and R2 score. The model founds to perform well number of elements in the test dataset is minimal
and so this model can predict the seizure values only a few seconds earlier.
Design of Self Powered Internet of Medical Things Using Robust Wolf Optimization Based PI Controller for Health Care Monitoring System
Authors: Karuppasamy C. , Venkatanarayanan S.
Published: January 2022

In order to gather, transmit, and develop input from the patients for monitoring their health condition through smart devices or devices which use embedded systems, such as processors and transducers and equipment for communication in the healthcare system, the Internet of Medical Things
(IoMT) maintains a huge network infrastructure. These devices therefore comprise of a powerful, scalable, lightweight storage knot, which requires power and batteries to run from a practical standpoint. The above shows that the energy collection plays a significant part in the enhancement
of IoMT devices’ efficiency and lifespan for its application in healthcare systems. Moreover, in view of the energy acquisition from the operational environment, energy collection is required to make the IoMT devices network more ecologically sustainable. In large solar PV generating
systems, partly shading situations usually develop, causing system losses. Thus, in power-voltage curves characteristic of solar systems, the appearance of several peak levels is conceivable. These kinds of problems can be handled by using new multilayer link inverter monitoring techniques.
A Maximum Point Tracking Scheme (MPPT) is being suggested for self-proposed Internet of Medical Things for the purpose of optimizing harvesting of solar power on entire PV chain with the usage of RGWO (Robust Wolf Optimization) dependent PI with PWM. The mistaken PV error might create inconsistent
power supply to the 7-level H-bridge inverter linked to a grid. The modulation compensation is included in the control system in order to stabilize the grid power. The suggested technique is applied to a 7-level inverter under partial shade conditions. The multi-level modular H-bridge inverter
is used for the grid-linked PV system. In addition to a DC link across all H-bridges, a short PV panel string is used for feeding each phase of n H-bridge converters which is connected in series. For pulse switching inverters, the usage of RGWO-based PI with PWM is used. The PWM is used. Then
L filters used to reduce the switch harmonics found in the grid are used to link the Cascade multilevel inverter with the grid. A seven-level threephase inverter with three H-bridges allows the individual MPPT control need. The harvester is under direct sunlight and sometimes overcast circumstances
realistically tested outside. The wearable IoMT sensor node uses a mean power of 20, 23 mW in a wake-up mode for one hour, and the node’s service life is 28 hours. The performance analysis is finally performed and MATLAB/SIMULINK simulation is performed.
Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeast United Sates
Authors: Steinschneider Scott , Najibi Nasser
Published: January 2022

This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.
What Could Interfere with a Good Night’s Sleep? The Risks of Social Isolation, Poor Physical and Psychological Health among Older Adults in China
Authors: Zhang Dan , Lin Zhiyong , Chen Feinian , Li Shuzhuo
Published: January 2022

This study provides one of the first population-based investigations of the longitudinal association between social isolation and sleep difficulty among older adults in China. We analyzed three waves of longitudinal data from the China Longitudinal Aging Social Survey (2014–2018), in which 8456 respondents contributed 16,156 person-year observations. Results from multilevel logistic regression models showed that social isolation was related to a higher risk of sleep difficulty. We also found that socially isolated older adults were more likely to report higher levels of depressive symptoms, a greater prevalence of loneliness and pain, and more chronic diseases compared to their socially integrated counterparts, which in turn increased their risks of sleep difficulty. Moreover, socially isolated older adults with chronic diseases were particularly vulnerable to the risk of sleep difficulty. These findings provide helpful guidance for policymakers and practitioners to design effective intervention strategies to help older adults with sleep problems.
The Potential for Sustainable Organic Farming in Nagaland: A Case Study
Authors: Sarkar Devpriya
Published: January 2022

In 2015, Sikkim, a North-Eastern state of India, achieved the state of being fully organic. Later, states like Nagaland, Mizoram, Meghalaya, Goa and Kerala have declared their intentions to be fully organic. In Nagaland, more than 47% of the population is engaged in agriculture and entirely dependent on the traditional mode of farming and has used organic manure like cattle dung, dried leaves-litter and crop residues for enhancing the capacity of soil from time immemorial. Also, studies have shown that the state of Nagaland has negligible use of inorganic supplements in their fields. Thus, Nagaland has a high potential to be converted into an organic state without making any significant shifts in their existing farming practices. Shifting cultivation, locally known as Jhum-kheti, is one of the oldest forms of the agricultural process in practice in Nagaland. However, some studies regard Jhum cultivation as harmful to the environment, but there is a scope to reinvent this farming method and move towards a more sustainable form of agriculture there. This study explores the relation between traditional farming and organic farming and the benefits of state-induced organic farming methods and their effects on the farmers of Nagaland. A survey was carried out in the Mokokchung district of Nagaland to understand the role of farmers in attaining sustainability.
Ethnocentrism and University Students in Ethiopia (1995–2018)
Authors: Amsalu Desalegn , Mesfin Seyoum
Published: January 2022

Since 1995 and until it apparently eased with the coming of Prime Minister Abiy Ahmed, ethnicity has become the dogmatic principle of the country for its social and political policies. Consequently, it has permeated through university campuses and affected relationships among students belonging to different ethnic groups. This study makes a micro-sociological ethnographic description and analysis of different “sites” of student interactions in curricular activities such as in classroom learning, extracurricular activities such as sporting, and service deliveries such as common residential compounds and dining halls at Addis Ababa University’s Main Campus.
Developing and Validating a Facial Emotion Recognition Task With Graded Intensity
Authors: Williams Trevor F. , Vehabovic Niko , Simms Leonard J.
Published: January 2022

Facial emotion recognition (FER) tasks are often digitally altered to vary expression intensity; however, such tasks have unknown psychometric properties. In these studies, an FER task was developed and validated—the Graded Emotional Face Task (GEFT)—which provided an opportunity to examine the psychometric properties of such tasks. Facial expressions were altered to produce five intensity levels for six emotions (e.g., 40% anger). In Study 1, 224 undergraduates viewed subsets of these faces and labeled the expressions. An item selection algorithm was used to maximize internal consistency and balance gender and ethnicity. In Study 2, 219 undergraduates completed the final GEFT and a multimethod battery of validity measures. Finally, in Study 3, 407 undergraduates oversampled for borderline personality disorder (BPD) completed the GEFT and a self-report BPD measure. Broad FER scales (e.g., overall anger) demonstrated evidence of reliability and validity; however, more specific subscales (e.g., 40% anger) had more variable psychometric properties. Notably, ceiling/floor effects appeared to decrease both internal consistency and limit external validity correlations. The findings are discussed from the perspective of measurement issues in the social cognition literature.
Keep your distance: Different roles for knowledge and affect in predicting social distancing behavior
Authors: Choi Soyeon , Merrell Wilson N , Ackerman Joshua M
Published: January 2022

The current study examines predictors of social distancing behavior across populations (students and community members) and across time in the early months of the COVID-19 pandemic, focusing on two factors commonly associated with risk perception and prevention: knowledge and affect. Results showed that, despite similar levels of social distancing, student distancing was predicted only by feelings of threat about COVID-19, whereas community distancing was predicted by both feeling informed and threatened. Examining longitudinal effects, which were limited to students only, students became more informed about COVID-19 over time, and increases in being informed (but not feeling threatened) predicted more distancing.
The affective embeddings of gacha games: Aesthetic assemblages and the mediated expression of the self
Authors: Woods Orlando
Published: January 2022

This article argues that the motivations for investing money in gacha games can be a function of the affective embedding of players within the game, and the game within broader circuits of cultural affinity and appeal. While research on gacha games – and the specific role of loot boxes therein – has emphasised their associations with gambling, I contend that affect is another trigger that can motivate seemingly irrational playing behaviours. The affective embeddings of gacha games motivate players to curate aesthetic assemblages of virtual content that enable the mediated expression of the self. Drawing on qualitative data generated among young Singapore-based players of gacha games, I explore how the acquisition of characters, skins and collections can be motivated by the emotional payoff that comes from relationality rather than gambling.
Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression
Authors: Mohammad Aran , Rezaei Reza , Hayduk Christopher , Delebinski Thaddaeus , Shahpouri Saeid , Shahbakhti Mahdi
Published: January 2022

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.
Experimental study of the effect of various collision angles and critical conditions on marine engine’s twin-spray collision process
Authors: Xia Jin , Zhang Qiankun , Wang Jianping , He Zhuoyao , Zhou Qiyan , Zhou Dezhi , Qian Yong , Ju Dehao , Lu Xingcai
Published: January 2022

To enhance the fuel-gas mixing and phase transition process, the fuel is injected by twin injectors in a large-bore low-speed two-stroke marine engine, while the cylinder condition has reached the transcritical and supercritical conditions. The twin-injector configuration has a great potential for further optimization, but the exploration on the outcome of collision and phase transition was still limited. Therefore, this work aims to study the effect of various collision angles (60°, 90°, 120°, 150°) and critical conditions (sub/trans/supercritical) on the twin-spray collision process using optical techniques. A wide range of experimental cases are conducted to provide an analysis and database for future modeling validation. The post-collisional spray structures, spatial distribution, and periphery features are analyzed to characterize the droplet’s collision. The results show that with the collision angle increasing, the higher collision velocity enhances the mass transfer while the minor vertical component results in a smaller axial dispersion. Because of the trade-off relationship between the vertical velocity component and pre-collision penetration, a higher reduction in droplet momentum results in a slighter collision behavior. At the collision angle of 150°, the subcritical condition tends to result in an off-axis collision. Under the transcritical (P) condition, the probability of head-on collision increases and presents a wider spatial distribution. But under the supercritical condition, because of the existence of the liquid collision, the thermal conversion among phases is accelerated, while the ambient resistance is reduced. Moreover, an exponential correlation of collision liquid length is formulated to predict the axial dispersion based on various critical conditions.
Hybrid Peacebuilding in Bosnia and Herzegovina: Participatory Arts and Youth Activism as Vehicles of Social Change
Authors: Redwood Henry , Fairey Tiffany , Hasić Jasmin
Published: January 2022

This article provides an analytical case study of a participatory youth-led filmmaking project in Bosnia and Herzegovina. Using the conceptual framework of hybridity, it critically considers whether and to what extent youth centred, participatory arts projects can facilitate the emergence of a positive hybrid peace. It reflects on three themes—solidarity; creativity as politics; and participation as norm—that speak to the opportunities and challenges encountered during the project. The analysis demonstrates that while participatory arts have the potential to induce a more emancipatory vision of peace, that, and mirroring the warnings from development studies, their effects are not a given and challenges and blockages persist.