bluuebunny/crossref_metadata_2025 huggingface dataset:
KECERDASAN EMOSI DENGAN FORGIVENESS PADA SANTRI PONDOK PESANTREN BANU HASYIM SIDOARJO
Authors: Zuroida Aironi , Purwaningtyas Fifin Dwi , Ramadhan Ilham Yusril , Lintang Eka Ananda
Published: November 2022

Pesantren merupakan salah satu pendidikan informal dan sekaligus juga terdapat pendidikan formal telah berfungsi sebagai pengembangan diri santri. Santri menghabiskan waktu kesehariannya di pondok pesantren dengan mengikuti kegiatan yang ada disana dan intekasi sosial mereka terbentuk dari aktivitas mereka yang hampir dilaksanakan secara bersama-sama sehingga tak dapat dipungkiri terjadi masalah dalam interaksi antara satu dengan yang lainnya yang dapat meningkalkan luka batin maupun kemarahan dalam hati. Oleh karenanya perlu adanya kecerdasan emosi supaya dapat membangun sikap forgiveness. Subjek dalam penelitian ini sebanyak 36 orang yang merupakan santri Banu Hasyim. Teknik analisa yang dilakukan adalah uji korelasi dengan product moment menggunakan SPSS. Hasil dari penelitian menunjukkan bahwa terdapat hubungan positif yang signifikan antara kecerdasan emosi dan forgiveness, semakin tinggi kecerdasan emosi santri maka semakin tinggi pula sikap forgiveness pada santri dengan nilai signifikansi sebesar 0,011. Dalam penelitian ini juga didapati hasil bahwa rata-rata santri memiliki kecerdasan emosi pada kategori tinggi yakni 75% dan sangat tinggi sebesar 19,5%, sedangkan pada forgiveness rata-rata santri juga berada pada kategori tinggi yakni 52% dan kategori sangat tinggi sebesar 48%. santri yang memiliki kecerdasan emosi tinggi maka ia akan bisa lebih tenang dalam menghadapi permasalahan maupun pengalaman-pengalaman yang menyakitkan dirinya, sehingga akan lebih mudah bersikap forgiveness.
GAYA HIDUP HEDONIS DAN PERILAKU KONSUMTIF PADA MAHASISWI FAKULTAS PSIKOLOGI UNIVERSITAS WIJAYA PUTRA SURABAYA
Authors: Ulfa Ita , Mardiyanti Ressy , Ristanti Evi
Published: November 2022

Perkembangan online shop di Indonesia sangat pesat, baik itu melalui marketplace ataupun media social. Saat ini media social, terutama instagram semakin sering digunakan sebagai media untuk berjualan, karena lebih menarik fiturnya dan lebih mudah mempengaruhi para viewer. instagram mulai menjadi bagian dari gaya hidup para remaja khususnya para pelajar atau mahasiswa. Mereka menduplikat gaya seseorang yang menurutnya sangat baik dalam berpenampilan. Melihat apa yang dikenakan dan mencoba untuk menirunya dengan harapan bisa menjadi seperti yang ditirunya. Perilaku ini mendorong seseorang untuk membeli apapun yang digunakan oleh selebgram, bahkan ada yang mengarah pada perilaku konsumtif. Tujuan penelitian ini adalah untuk mengetahui tentang pengaruh gaya hidup hedonis terhadap perilaku konsumtif mahasiswi psikologi Wijaya Putra Surabaya. Penelitian ini menggunakan metode penelitian kuantitatif, dengan memperhatikan uji normalitas dan linieritas data, dan pengujian hipotesisnya menggunakan analisis regresi sederhana dengan bantuan software SPSS 20. Teknik pengambilan sampel dengan cara purposive sampling dan hasil olah data menunjukkan taraf signifikansi 0,007< 0,05. Hal ini memiliki arti bahwa ada pengaruh gaya hidup hedonis dengan perilaku konsumtif pada mahasiswi psikologi universitas Wijaya putra. Kata kunci : Hedonis, Perilaku Konsumtif
ANALISIS RESEPSI FOLLOWERS AKUN @Roughneck1991 TERHADAP KONTEN “Roughneck Shopee 11.11”
Authors: Anindhita Martha , Prawiradiredja Sanhari , Angga Citra
Published: November 2022

Penelitian ini dilatar belakangi dengan semakin berkembangnya industri fashion dan juga pengguna internet. Dimana semakin kesini, para pengguna platform sosial media semakin bertambah. Dengan begitu brand Roughneck1991 memanfaatkan platform sosial media Instagram untuk melakukan promosi. Salah satu nya ialah dengan membuat konten Instagram pada saat berlangsungnya acara Shopee 11.11. Metode yang digunakan dalam penelitian ini adalah metode Analisis Resepsi. Teknik pengumpulan data yang digunakakan dalam penelitian ini adalah pengamatan, dan wawancara. Selanjutnya informan yang ada dalam penelitian ini berjumlah 10 orang dari followers akun instagram @Roughneck1991. Dengan adanya kecanggihan digital saat ini di harapkan para brand Roughneck dapat semakin mendunia dalam penjualannya.
REPRESENTASI PENDIDIKAN SEKS DALAM FILM LITTLE MOM (ANALISIS SEMIOTIKA ROLAND BARTHES)
Authors: Kahu Maria Oktaviani , Panuju Redi , Syueb Sudono , Siska Siska
Published: November 2022

Penelitian ini bertujuan untuk menggambarkan representasi pendidikan seks dalam film little mom yang diproduksi oleh Hitamaker Studios. Jenis penelitian yang dingunakan oleh penulis dalam penelitian ini adalah penelitian kualitatif deskriptif. Metode dari penelitian ini menggunakan analisis semiotika Roland Barthes dengan menggunakan signifikan dua arah yaitu pemaknaan denotasi. Konotasi dan mitos. Film merupakan media komunikasi audio-visual untuk menyampaikan pesan kepada kelompok orang. Kekuatan serta kemampuan film mampu menjangkau banyak segmen social, yang membuat para praktisi film memiliki potensi untuk mempengaruhi atau membentuk suatu pandangan suatu khayalak dengan muatan pesan didalamnya. Dengan adanya masalah yang ada dalam film Little Mom yaitu terjadinya seks pranika sehingga menyebabkan kehamilan yang tidak direncanakan menggambarkan bahwa film juga salah satu cara untuk mengungkapkan pentingnya pendidikan seks serta peran orangtua dalam menginformasikan kepada anak. Subjek dari penelitia ini adalah pemeran utama dari film little mom yaitu, Naura dan Yuda, Objek dari penelitian ini adalah tanda pada film little mom yang dipresentasikan dalam adegan, dialog dan karakter dalam film. Hasil dari penelitian ini menunjukan bahwa terdapat gambaran tentang pendidikan seks dan mengetahui konsekuensi dari setiap perbuatan yang berhubungan dengan seks.
Socio-Economic Realities of Returned Immigrant Reintegration in Ghana: A Systematic Review
Authors: Nartey Aaron
Published: November 2022

The purpose of this study was to elaborate on the different dimensions to conduct an online assessment in undergraduate mathematics. Mathematics assessment using digital technologies is unique because it has special symbols and multi-step solutions. The sudden shift to full online learning necessitated Learning Management System-based online assessment as part of learning and for certification. The research design for this study was a qualitative case study. The case considered for this study was a calculus course and the 180 students registered for the course in 2021 at a South African university. The researcher was the instructor for this course. After explaining the details of conducting the online assessment of mathematics, data was collected on students’ experiences in online assessment using questionnaires and interviews. A conveniently chosen sample of 13 students completed the online questionnaire. A further ten students took part in the telephonic interviews and these were selected conveniently again. Data was composed of written responses to the questionnaires and the transcriptions of the interview audio recordings. After sitting both quizzes and assignments on Blackboard, students preferred the assignment format. This was also suitable for mathematics where there is a need to show steps and proofs in the solution process for both formative and summative assessment. Course instructors hence can effectively administer appropriate formative online assessments on the Learning Management System that has the potential to propel the teaching and learning of mathematics. Keywords: Learning Management System; undergraduate mathematics; online assessment; quizzes; assignments; Blackboard; formative assessment
STATISTICAL ANALYSIS OF THE ROLE OF WOMEN IN AGRICULTURAL PRODUCTION AND HOUSEHOLD-DECISION MAKING IN BUM CHIEFDOM
Authors: Tamba Issa Isaac , Seibureh Victor , Seppeh Sheku , James Turay Mathew
Published: November 2022

The purpose of this study is to investigate “The Role of Women in Agricultural Production and Household-Decision Making in Bum Chiefdom”. It has assessed the various ranges of activities that are performed by women and identified the overall drawbacks encountered by women. The survey was conducted in 16 villages in four sections of Bum Chiefdom in rural districts of Bonthe and 80 households by taking 75% women and 25% men in their residents or sometimes in their farm site. The interviewees were selected using a stratified random sampling technique picking up 16 households from each section. The data were analyzed through descriptive and inference statistics. The Pearson Chi-Square Test was used to the test relationship between variables. The results have shown that women have a primary role in providing labour for agricultural production and household works in the study area. Women play a significant role in the agricultural labour force and in household activities. As a result, their contribution to agricultural output is undoubtedly extremely significant. The most general conclusion reached from the study is that women spend more time than men in activities and agriculture tasks. In day-to-day agricultural transactions, the village men in Bum Chiefdom, southern Sierra Leone make more decisions than women. Division of Labour by gender in farming exists. Women perform their duties on the common plot and prepare food from what is designated for consumption from the farm products. To strengthen and develop women in economic and social, men, local authority government, and other concerned bodies should take all appropriate measures to ensure women empower and actively participate in deciding on agricultural activities, income/expenditure and household decision-making, without any discrimination. Women should also participate at all stages of project agricultural planning, implementation and assessment.
Ray-Singer Torsion and the Rumin Laplacian on Lens Space
Authors:   The University of Tokyo, Japan, Kitaoka Akira
Published: November 2022

We express explicitly the analytic torsion functions associated with the Rumin complex on lens spaces in terms of the Hurwitz zeta function. In particular, we find that the functions vanish at the origin and determine the analytic torsions. Moreover, we have a formula between this torsion and the Ray-Singer torsion.
Accretion from Winds of Red Giant Branch Stars May Reveal the Supermassive Black Hole in Leo I
Authors: Pacucci Fabio , Loeb Abraham
Published: November 2022

A supermassive black hole (SMBH) of ∼3 × 106
M
⊙ was recently detected via dynamical measurements at the center of the dwarf galaxy Leo I. Standing ∼2 orders of magnitude above standard scaling relations, this SMBH is hosted by a galaxy devoid of gas and with no significant star formation in the last ∼1 Gyr. This detection can profoundly impact the formation models for black holes and their hosts. We propose that winds from a population of ∼100 evolved stars within the Bondi radius of the SMBH produce a sizable accretion rate, with Eddington ratios between 9 × 10−8 and 9 × 10−7, depending on the value of the stellar mass loss. These rates are typical of SMBHs accreting in advection-dominated accretion flow mode. The predicted spectrum peaks in the microwaves at ∼0.1–1 THz (300–3000 μm) and exhibits significant variations at higher energies depending on the accretion rate. We predict a radio flux of ∼0.1 mJy at 6 GHz, mildly dependent on the accretion properties. Deep imaging with Chandra, the Very Large Array, and the Atacama Large Millimeter/submillimeter Array can confirm the presence of this SMBH and constrain its accretion flow.
Diverse Properties of Molecular Gas in the Host Galaxies of Fast Radio Bursts
Authors: Hatsukade Bunyo , Hashimoto Tetsuya , Niino Yuu , Hsu Tzu-Yin
Published: November 2022

We report the properties of molecular gas in a sample of six host galaxies of fast radio bursts (FRBs) obtained from CO observations with the Atacama Large Millimeter/submillimeter Array (FRBs 20180924B, 20190102C, and 20190711A), the results of one nondetection in a dwarf galaxy (FRB 20121102A), and two events detected in M81 (FRB 20200120E) and the Milky Way (FRB 20200428A). The CO observations resulted in the detection of CO(3–2) emission in the FRB 20180924B host and nondetections of CO(3–2) and CO(2–1) emission in the hosts of FRB 20190102C and FRB 20190711A, respectively. The derived molecular gas mass and 3σ upper limit is (2.4 ± 0.2) × 109
M
⊙, <3.8 × 108
M
⊙, and <6.7 × 109
M
⊙ for the hosts of FRB 20180924B, FRB 20190102C, and FRB 20190711A, respectively. We found diversity in molecular gas properties (gas mass, gas depletion time, and gas fraction to stellar mass) in the sample. Compared to other star-forming galaxies, the FRB 20180924B host is gas-rich (the larger molecular gas fraction), and the hosts of FRB 20190102C and FRB 20200120E are gas-poor with a shorter depletion time for their stellar mass and star formation rate. Our findings suggest that FRBs arise from multiple progenitors or single progenitors that can exist in a wide range of galaxy environments. Statistical analysis shows a significant difference in the distribution of molecular gas fraction between the FRB hosts and local star-forming galaxies. However, the difference is not substantial when an outlier, the FRB 20200120E host, is excluded, and analysis with a larger sample is needed.
Nanoflare Heating of the Solar Corona Observed in X-Rays
Authors: Upendran Vishal , Tripathi Durgesh , Mithun N. P. S. , Vadawale Santosh , Bhardwaj Anil
Published: November 2022

The existence of the million-degree corona above the cooler photosphere is an unsolved problem in astrophysics. Detailed study of the quiescent corona that exists regardless of the phase of the solar cycle may provide fruitful hints toward resolving this conundrum. However, the properties of heating mechanisms can be obtained only statistically in these regions due to their unresolved nature. Here, we develop a two-step inversion scheme based on the machine-learning scheme of Upendran & Tripathi (2021a) for the empirical impulsive heating model of Pauluhn & Solanki (2007), and apply it to disk integrated flux measurements of the quiet corona as measured by the X-ray solar monitor on board Chandrayaan-2. We use data in three energy passbands, viz, 1–1.3, 1.3–2.3, and 1–2.3 keV, and estimate the typical impulsive event frequencies, timescales, amplitudes, and the distribution of amplitudes. We find that the impulsive events occur at a frequency of ≈25 events per minute with a typical lifetime of ≈10 minutes. They are characterized by a power-law distribution with a slope α ≤ 2.0. The typical amplitudes of these events lie in an energy range of 1021–1024 erg, with a typical radiative loss of about ≈103 erg cm−2 s−1 in the energy range of 1–2.3 keV. These results provide further constraints on the properties of subpixel impulsive events in maintaining the quiet solar corona.
The Sparse Atmospheric Model Sampling Analysis (SAMOSA) Intercomparison: Motivations and Protocol Version 1.0: A CUISINES Model Intercomparison Project
Authors: Haqq-Misra Jacob , Wolf Eric T. , Fauchez Thomas J. , Shields Aomawa L. , Kopparapu Ravi K.
Published: November 2022

Planets in synchronous rotation around low-mass stars are the most salient targets for current ground- and space-based missions to observe and characterize. Such model calculations can help to prioritize targets for observation with current and future missions; however, intrinsic differences in the complexity and physical parameterizations of various models can lead to different predictions of a planet’s climate state. Understanding model differences is necessary if such models are to guide target selection and aid in the analysis of observations. This paper presents a protocol to intercompare models of a hypothetical planet with a 15-day synchronous rotation period around a 3000 K blackbody star across a parameter space of surface pressure and incident instellation. We conduct a sparse sample of 16 cases from a previously published exploration of this parameter space with the ExoPlaSim model. By selecting particular cases across this broad parameter space, the SAMOSA intercomparison will identify areas where simpler models are sufficient, as well as areas where more complex GCMs are required. Our preliminary comparison using ExoCAM shows general consistency between the climate state predicted by ExoCAM and ExoPlaSim except in regions of the parameter space most likely to be in a steam atmosphere or incipient runaway greenhouse state. We use this preliminary analysis to define several options for participation in the intercomparison by models of all levels of complexity. The participation of other GCMs is crucial to understand how the atmospheric states across this parameter space differ with model capabilities.
New Views of the Internal Structure of Planum Boreum from Enhanced 3D Imaging of Mars Reconnaissance Orbiter Shallow Radar Data
Authors: Putzig Nathaniel E. , Foss II Frederick J. , Campbell Bruce A. , Holt John W. , Perry Matthew R. , Smith Isaac B. , Russell Aaron T. , Nerozzi Stefano , Christoffersen Michael S. , Mueller Isabella H. , Sava Paul C.
Published: November 2022

We present first results from a new 3D radargram produced from 3399 Mars Reconnaissance Orbiter (MRO) Shallow Radar observations of the north polar region of Mars. While incorporating an additional 5 yr of observations relative to the prior 3D radargram, we employed surface-clutter simulations to improve the coregistration of the input data and thereby enhance the effective vertical resolution of features. Combining those improvements with the geometric corrections and an increase in signal-to-noise ratio afforded by the 3D imaging process, this data product provides new details about the interior of Planum Boreum, the Martian north polar cap. We assess the overall characteristics and compare portions of the new 3D radargram to results from prior studies that used either the prior 3D radargram or sets of 2D radargrams from individual MRO orbits. We find that the new 3D radargram has recovered essentially all of the vertical resolution inherent to the input data, and the increased coverage density has substantially reduced artifacts while enabling much greater detail in the imaging of subsurface layering and structures. These improvements extend throughout the 3D radargram, from the basal units to the shallowest subsurface layering in Planum Boreum, and out into the surrounding plains. Subsurface features such as a buried chasma, other layering structures and unconformities, and trough-bounding surfaces that offset shallow layering are now visible in unprecedented detail. A thorough analysis of this new 3D radargram and its implications for the geologic and climate history of Planum Boreum will extend over many years.
Building an integrated requirements engineering process based on Intelligent Systems and Semantic Reasoning on the basis of a systematic analysis of existing proposals
Authors: Corral Alexandra , Sánchez Luis E. , Antonelli Leandro
Published: November 2022

Requirements Engineering is one of the fundamental activities in the software development process and is oriented toward what should be produced. One of the development team’s most common problems is a lack of communication regarding an understanding of the discourse domain and how to integrate and process excessive information originating from different sources. This may lead to errors of omission and the consequent production of incomplete and inconsistent artifacts, which will have a direct effect on the quality of the software. The use of machine learning techniques helps the development team produce successful software on the basis of the acquisition of knowledge and human experience with which to understand the domain of the application. This paper, therefore, presents a proposal for a new methodological process oriented toward the construction of a vocabulary concerning the application domain. The authors propose to do this by employing Natural Language Processing (NLP), ontologies and heuristics that will lead to the production of a Lexicon that is common to analysts and customers, both of whom will understand the universe of discourse, thus mitigating problems of completeness. This objective has been achieved by carrying out a Systematic Literature Review of the artificial intelligence techniques employed in the requirements engineering process, which led to the discovery that 41.37% use NLP, while 55.71% apply ontologies such as semantic reasoners which help solve the problem of language ambiguity, the structures in specifications or the identification of key concepts with which to establish traceability links. However, the review also showed that the problems regarding the comprehension and completeness of requirements problems have yet to be resolved.
La recherche en gestion vue sous l’angle de la modélisation d’aide à la décision
Authors: Nakhla Michel
Published: November 2022

L’objectif de cet article est de proposer une synthèse des recherches consacrées à l’implémentation des modèles d’aide à la décision dans les entreprises et à leur validité organisationnelle. Il montre comment ces recherches peuvent, d’une part, orienter et stimuler l’exploration de nouvelles formes de modélisation et, d’autre part, mettre en évidence les connaissances permettant l’émergence de nouvelles formes d’organisation. Il discute également des apports croisés entre ingénierie et gestion, implémentation des modèles et sciences de gestion.
Improving Rates of Pneumococcal Vaccination in a Skilled Nursing Facility
Authors: Helms Jonathan , Reinbeck Donna
Published: November 2022

The pneumonia vaccine is the best way to prevent pneumococcal disease and the high cost of readmission for patients and payors. The readmission rate at a skilled nursing facility (SNF) for a patient with a diagnosis of pneumonia was 24%, whereas the vaccination rate at the SNF was only 28%. This finding is significant because patients aged ≥65 years have a 35% reduction in pneumonia influenzas and all-cause mortality with appropriate pneumococcal vaccination. The current initiative sought to increase the pneumococcal vaccine rate at a SNF through implementation of a structured vaccine initiative. After implementation of a pneumococcal vaccination bundle, vaccine uptake increased from 28% to 66%. The success of the pneumococcal vaccination bundle showed that a focused effort using context-specific, evidence-based interventions can increase vaccine uptake. Adapting an evidence-based bundle to fit the needs of the user's context is an important component to the success of vaccination initiatives. [
Journal of Gerontological Nursing, 48
(12), 52–56.]
Congenital Cranial Dysinnervation Disorder Complicated by Ipsilateral Orbital Choriostoma
Authors: Khan Arif O.
Published: November 2022

A 20-year-old woman with right-sided congenital cranial dysinnervation disorder (exotropia, ophthalmoplegia, and ptosis) presented for strabismus surgery. Exploration of the right eye confirmed abnormal extraocular muscles. Lateral rectus recession with medial rectus plication and inferior oblique recession improved primary position ocular alignment. However, an unusual pattern of inferior chemosis developed postoperatively and persisted during the next month. Excision and histologic examination of this tissue revealed areas of lacrimal gland and cartilage with surrounding inflammation, suggestive of a disrupted orbital choriostoma.

[
J Pediatr Ophthalmol Strabismus
. 2022;59(6):e66–e68.]
A characterization and implementation of corank one map germs from $ 2 $-space to $ 3 $-space in the computer algebra system SINGULAR
Authors: Wang Ying , Binyamin Muhammad Ahsan , Iqbal Tauqeer , Aslam Saima , Aslam Adnan
Published: November 2022

The classification and the geometry of corank one map germs from $ ({\mathbb{C}}^2, 0) \rightarrow ({\mathbb{C}}^3, 0) $ have been studied by Mond [1,2]. In this paper we characterize the classification of map germs of corank at most $ 1 $, in terms of certain invariants. Moreover, by using this characterization, we develop an algorithm to compute the type of map germs with out computing the normal form. Also, we give its implementation in the computer algebra system SINGULAR [15].
Weapon operating pose detection and suspicious human activity classification using skeleton graphs
Authors: Bhatt Anant , Ganatra Amit
Published: November 2022

Spurt upsurge in violent protest and armed conflict in populous, civil areas has upstretched momentous concern worldwide. The unrelenting strategy of the law enforcement agencies focuses on thwarting the conspicuous impact of violent events. Increased surveillance using a widespread visual network supports the state actors in maintaining vigilance. Minute, simultaneous monitoring of numerous surveillance feeds is a workforce-intensive, idiosyncratic, and otiose method. Significant advancements in Machine Learning (ML) show potential in realizing precise models to detect suspicious activities in the mob. Existing pose estimation techniques have privations in detecting weapon operation activity. The paper proposes a comprehensive, customized human activity recognition approach using human body skeleton graphs. The VGG-19 backbone extracted 6600 body coordinates from the customized dataset. The methodology categorizes human activities into eight classes experienced during violent clashes. It facilitates alarm triggers in a specific activity, i.e., stone pelting or weapon handling while walking, standing, and kneeling is considered a regular activity. The end-to-end pipeline presents a robust model for multiple human tracking, mapping a skeleton graph for each person in consecutive surveillance video frames with the improved categorization of suspicious human activities, realizing effective crowd management. LSTM-RNN Network, trained on a customized dataset superimposed with Kalman filter, attained 89.09% accuracy for real-time pose identification.
Learning Cultural Spaces: A Collaborative Creation of a Virtual Art Museum Using Roblox
Authors: Kang Donghyun , Choi Haram , Nam SangHun
Published: November 2022

This study proposes education on creating virtual art museums using metaverse technology to broaden the understanding of art museums. It investigates the effect of creating a virtual art museum using Roblox Studio, a metaverse platform, on the acquisition of knowledge about art museums, artists, and artworks and the ability to create metaverse content. This study selected the Moonshin Art Museum (MAM), the art museum to be created in the metaverse space, as its local cultural space. Fifteen students participated in a creation workshop to learn to create virtual spatial content using Roblox Studio and also visited an actual art museum. The students were then assigned to architecture, artwork, avatar, and content teams and collaborated with one another. To evaluate the activities, students filled out pre- and post-questionnaires containing items about the MAM, the sculptor Moonshin, and Moonshin’s artwork, as well as the ability to produce metaverse content. Findings showed that creating a virtual art museum through a metaverse platform facilitates the acquisition of spatial knowledge about art museums as well as information about artists and artworks. In addition, collaboration not only helped in the creation task but also identified and solved technical difficulties and improved creative abilities. These results suggest possibilities for using metaverse technology in delivering education regarding virtual art museums.
Impact of the Flipped Classroom on the Motivation of Undergraduate Students of the Higher Institute of Nursing Professions and Health Techniques of Fez-Morocco
Authors: Lamsyah Rajae , El Bouazzaoui Abderrahim , Kanjaa Nabil
Published: November 2022

Motivation is the cornerstone in building success. If it is strong, the achievement of a greater number of objectives will be assured, and therefore a more glorious success can be met. Thus, to promote motivation, it is advisable to pay particular attention to the pedagogical approach adopted by the teacher, since it has been proven that there is a positive relationship between the choice of an active approach and an enhancing motivation. The situation generated by COVID-19, has been a profitable opportunity to experiment with hybrid “Flipped Classroom” pedagogy, with a view to inverting the class and adapting learning activities, that have been traditionally offered to students, through distance (online) and in-person learning alternately. Thus, the present study aims at evaluating the impact of this Flipped Pedagogy on the motivation of undergraduate students of the Higher Institute of Nursing Professions and Healthcare Techniques in Fez. Data have been collected from 410 students, who were questioned before and after the adoption of the Flipped Classroom, based on a motivation scale validated by Rolland Viau in 1994. The answering percentage was of 90.73%. Data analysis demonstrated the existence of a significant relationship between the Flipped Classroom and students' motivation; the motivational profile, which was 18.54%, became 89.25% after flipping the class. The Flipped Classroom can significantly bring a surplus value to pedagogy, hence the importance of broadening its spectrum and integrating it as a new approach of teaching within health sciences educational institutions.
Machine Learning-Based Evaluation of Information Literacy Enhancement among College Teachers
Authors: Li Juan
Published: November 2022

To enhance the information literacy among college teachers, it is necessary to evaluate their existing information awareness, information ethics, information techniques and information competence. Existing studies qualitatively analysed the effective means of enhancing information literacy among college teachers, and the dimensions were too homogeneous. In response, this paper studies the machine learning-based evaluation of college teachers’ information literacy enhancement. Firstly, the paper presents a framework of predictive model on information literacy enhancement evaluation of college teachers, and the influencing factors of college teachers’ information technology usage behaviour (ITUB) from the authors’ viewpoint. Then the paper presents a framework diagram for extracting ITUB features, along with a detailed introduction to specific influencing factors. After that, the paper extracts the potential information in the content of information technology behaviour to be predicted. The content features of ITUB are characterised by two aspects: content similarity and data form features. Next, the paper shows a method for calculating the affective polarity of ITUB. It also constructs a predictive model for enhancing ITUB and shows the objective function of the model. The experimental results verify the validity of the constructed model.
A Snapshot of Hate: Subjective Psychological Distress After a Hate Crime: An Exploratory Study on Victimization of Muslims in Canada
Authors: Kennedy-Turner Kathleen , Côté-Lussier Carolyn , Helly Denise
Published: November 2022

Across Canada, hate crimes, especially those motivated by race, ethnicity, or religion, are still prevalent. For example, in 2019, 46% of police-reported hate crimes were motivated by race or ethnicity, and 32% were motivated by religion (Moreau, 2021). In Canada, Muslims are the second most targeted religious group in terms of hate crimes. However, Canadian research on the nature of hate crime victimization amongst Muslims and the impacts on their health and well-being is limited. The present study sought to use exploratory survey data to assess the demographic characteristics of those experiencing both verbal and physical assaults based on their religion. Further, we assessed whether those that experienced these assaults also experienced psychological distress (such as feeling nervous or hopeless). Based on a sample of 230 participants (58% women), it was found that individuals that self-identified as visibly Muslim were 3 times more likely, and those living in Vancouver were 9 times more likely, to report having been physically assaulted. Furthermore, having been physically assaulted, being a woman, residing in Vancouver, or self-identifying as visibly Muslim were factors associated with higher levels of psychological distress. This study is the first of its kind exploring the effects of hate crimes on Muslims across Canada. The impacts of hate crime on the psychological well-being of this marginalized population, especially for Muslim women, suggests a need for more research on the psychological distress of these individuals
Metaverse
Authors: Vishkaei Behzad Maleki
Published: November 2022

The way we interact with the physical world around us is being changed rapidly by advances in technology. These new technologies are very helpful for developing smart cities with the goal of improving the quality of life considering sustainability. Technology is also at the core of the circular city vision, and we need to use the new technologies and act in a smarter way in smart cities to support the achievement of economic, environmental, and social targets that are important for sustainable development and circular economy (CE) implementation. The metaverse (which is the combination of the prefix “meta” with the word “universe”) is a new concept that is known as transcending hypothetical synthetic environment linked to the physical world. In this chapter, the proposed concept will be introduced in more detail, and then the authors discuss how smart cities can use the metaverse platforms for improving themselves in five main aspects including governance, environment, mobility, economy, and quality of life dimensions.
Fairness and Ethics in Artificial Intelligence-Based Medical Imaging
Authors: Tripathi Satvik , Musiolik Thomas Heinrich
Published: November 2022

Artificial intelligence has a huge array of current and potential applications in healthcare and medicine. Ethical issues arising due to algorithmic biases are one of the greatest challenges faced in the generalizability of AI models today. The authors address safety and regulatory barriers that impede data sharing in medicine as well as potential changes to existing techniques and frameworks that might allow ethical data sharing for machine learning. With these developments in view, they also present different algorithmic models that are being used to develop machine learning-based medical systems that will potentially evolve to be free of the sample, annotator, and temporal bias. These AI-based medical imaging models will then be completely implemented in healthcare facilities and institutions all around the world, even in the remotest areas, making diagnosis and patient care both cheaper and freely accessible.
Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation
Authors: Mani V. R. S.
Published: November 2022

In this chapter, the author paints a comprehensive picture of different deep learning models used in different multi-modal image segmentation tasks. This chapter is an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different types of multi-modal images and the corresponding types of convolution neural networks used in the segmentation task. The chapter starts with an introduction to CNN topology and describes various models like Hyper Dense Net, Organ Attention Net, UNet, VNet, Dilated Fully Convolutional Network, Transfer Learning, etc.
Automatic Detection of Irritable Bowel Syndrome for 3D Images Using Supervoxel and Graph Cut Algorithm
Authors: Vaithianathan Geetha , Rajkumar E.
Published: November 2022

Medical image processing is a complex exercise and involves a number of stages to identify the disease in the arena of medical imaging. Irritable bowel syndrome is an acute disorder that causes intense abdominal pain and leads to changes in the bowel system. It gives rise to various indications like bleeding, bloating, celiac disease, gastric cancer, ulcer, etc. The system proposed here seeks to segment and classify each symptom of the irritable bowel syndrome individually with the aid of supervoxel segmentation algorithm. Features are extracted depending on the color, shape, and texture of the object. The extracted features are fed into the multi-support vector machine to identify the specific region in the medical image. The experiment provides the result of a test set 100 images stored in the data set which improves accuracy that refines the final output.
DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion
Authors: Talbi Hassiba , Kholladi Mohamed-Khireddine
Published: November 2022

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.
Feature Selection Using Random Forest Algorithm to Diagnose Tuberculosis From Lung CT Images
Authors: Rajendran Beaulah Jeyavathana , K. V. Kanimozhi
Published: November 2022

Tuberculosis is one of the hazardous infectious diseases that can be categorized by the evolution of tubercles in the tissues. This disease mainly affects the lungs and also the other parts of the body. The disease can be easily diagnosed by the radiologists. The main objective of this chapter is to get best solution selected by means of modified particle swarm optimization is regarded as optimal feature descriptor. Five stages are being used to detect tuberculosis disease. They are pre-processing an image, segmenting the lungs and extracting the feature, feature selection and classification. These stages that are used in medical image processing to identify the tuberculosis. In the feature extraction, the GLCM approach is used to extract the features and from the extracted feature sets the optimal features are selected by random forest. Finally, support vector machine classifier method is used for image classification. The experimentation is done, and intermediate results are obtained. The proposed system accuracy results are better than the existing method in classification.
GUI-CAD Tool for Segmentation and Classification of Abnormalities in Lung CT Image
Authors: Kishore V. Vijaya , Satyanarayana R.V.S.
Published: November 2022

A vital necessity for clinical determination and treatment is an opportunity to prepare a procedure that is universally adaptable. Computer aided diagnosis (CAD) of various medical conditions has seen a tremendous growth in recent years. The frameworks combined with expanding capacity, the coliseum of CAD is touching new spaces. The goal of proposed work is to build an easy to understand multifunctional GUI Device for CAD that performs intelligent preparing of lung CT images. Functions implemented are to achieve region of interest (ROI) segmentation for nodule detection. The nodule extraction from ROI is implemented by morphological operations, reducing the complexity and making the system suitable for real-time applications. In addition, an interactive 3D viewer and performance measure tool that quantifies and measures the nodules is integrated. The results are validated through clinical expert. This serves as a foundation to determine, the decision of treatment and the prospect of recovery.
Texture Segmentation and Features of Medical Images
Authors: Yadav Ashwani Kumar , Vaishali  , Kumar Raj , Kumar Archek Praveen
Published: November 2022

Texture analysis is one of the basic aspects of human visual system by which one can differentiate the objects and homogenous areas in an image. Manual diagnosis is not possible for huge database of images. Automatic diagnosis is required for greater accuracy in a shorter time. Texture analysis is required for effective diagnosis of medical images like functional MRI (magnetic resonance image) and diffusion tensor MRI, where only visualization is not sufficient to get the pathological information. This chapter explains the basic concepts of texture analysis and features available for analysis of medical images. Specifically, the intense review of texture segmentation and texture feature extraction and entropy measures of medical images have been done. The chapter also explores the available techniques for it. Common findings, comparative analysis, and gaps identified have also been mentioned on both issues.
A Comparative Study of Medical Image Retrieval Using Distance, Transform, Texture, and Shape
Authors: Swarnambiga A. , Vasuki S.
Published: November 2022

Content-based medical image retrieval (CBMIR) is the application of computer vision techniques to the problem of medical image search in large databases. Three main techniques are applied to check the applicability. The first technique implemented is distance metrics-based retrieval. The second technique implemented is transform-based retrieval. The transform which has lesser performance is combined with higher performance, to check the applicability of the results. The third technique implemented is content-based medical image retrieval. Texture and shape-based retrieval techniques are also applied. Shape-based retrieval is processed using canny edge with the Otsu method. The multifeature-based technique is also applied and analyzed. The best retrieval rate is achieved by multifeature-based retrieval with 100/50%. Based on more relevant retrieved images all the three, brain, liver, and knee, images are found to be retrieved more with 100/50%.
Enchodroma Tumor Detection From MRI Images Using SVM Classifier
Authors: Durgadevi G. , Sujatha K. , Thivya K.S. , Elakkiya S. , Anand M. , Shobana S.
Published: November 2022

Magnetic resonance imaging is a standard modality used in medicine for bone diagnosis and treatment. It offers the advantage to be a non-invasive technique that enables the analysis of bone tissues. The early detection of tumor in the bone leads on saving the patients' life through proper care. The accurate detection of tumor in the MRI scans are very easy to perform. Furthermore, the tumor detection in an image is useful not only for medical experts, but also for other purposes like segmentation and 3D reconstruction. The manual delineation and visual inspection will be limited to avoid time consumption by medical doctors. The bone tumor tissue detection allows localizing a mass of abnormal cells in a slice of magnetic resonance (MR).
Deep Convolutional Neural Network-Based Analysis for Breast Cancer Histology Images
Authors: Kumar E. Sudheer , Bindu C. Shoba , Madhu Sirivella
Published: November 2022

Breast cancer is one of the main causes of cancer death worldwide, and early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious. The relevance and potential of automatic classification algorithms using Hematoxylin-Eosin stained histopathological images have already been demonstrated, but the reported results are still sub-optimal for clinical use. Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. Based on the predominant cancer type, the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. The convolutional neural networks (CNN) is proposed to retrieve information at different scales, including both nuclei and overall tissue organization. This chapter utilizes several deep neural network architectures and gradient boosted trees classifier to classify the histology images among four classes. Hence, this approach has outperformed existing approaches in terms of accuracy and implementation complexity.
Digital Recognition of Breast Cancer Using TakhisisNet
Authors: Nanni Loris , Lumini Alessandra , Maguolo Gianluca
Published: November 2022

In this chapter, the authors evaluate several basic image processing and advanced image pattern recognition techniques for automatically analyzing bioimages, with the aim of designing different ensembles of canonical and deep classifiers for breast lesion classification in ultrasound images. The analysis starts from convolutional neural networks (CNNs) in a square matrix that is used to feed other CNNs. The novel ensemble, named TakhisisNet, is the combination by sum rule of the whole set of the modified CNNs and the original one. Moreover, the performance of the system is further improved by combining it with some handcrafted features. Experimental results obtained on the well-known OASBUD breast cancer dataset (i.e., the open access series of breast ultrasonic data) and on a large set of bioimage classification problems show that TakhisisNet obtains very valuable results and outperforms other approaches previously tested in the same datasets.
Groupwise Non-Rigid Image Alignment Using Few Parameters
Authors: Aal-Yhia Ahmad Hashim , Tiddeman Bernard , Malcolm Paul , Zwiggelaar Reyer
Published: November 2022

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is efficient, has very few free parameters to tune, and the authors show how to tune the few remaining parameters. Results show that the method reliably aligns various datasets including two facial datasets and two medical datasets of prostate and brain MRI images and demonstrates efficiency in terms of performance and a reduction of the computational cost.
Finite Element Modeling of Upper Ball Joint in a Two-Step Hot Forging Process
Authors: Siripath Nattarawee , Suranuntchai Surasak , Sucharitpwatskul Sedthawatt
Published: November 2022

This paper presented an analysis of the two-step of hot forging process are carried out to manufacture Upper Ball joint, which are including roughing and finishing operation. The part was made from SNCM8 alloy steel. The simulation has been done with application of QForm V10.1.6 software. The constitutive model based on Zener-Hollomon parameter was applied. As a result of simulations, metal flow lines, plastic strain, temperature distribution and effective stress for forgings were obtained. Finite element simulation by QForm V10.1.6. software is suggested as a valuable tool for predicting the hot deformation behavior of material during multi-stage of hot forging process which utilized to enhance the manufacturing process. In addition, the forming load and thickness during the forging process were analyzed. It was found that the deviation of forming load between simulation and experiment was raised to 10.71% and the maximum error of flank thickness was 5.137%. within specification. Therefore, the workpieces of the quality required by specification are obtained.