bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of local potential-based workshop and entrepreneurship learning in increasing entrepreneurial interest
Authors: Pritasari Octaverina Kecvara , Marniati Marniati , Susila I Wayan
Published: March 2024

This research aims to determine the effect of local potential-based workshops and entrepreneurship learning in increasing the interest in entrepreneurship of grade XI students majoring in Fashion in 3 different vocational schools in East Java Province, Indonesia. This research uses a quantitative approach. Data collection techniques were observation, questionnaires, interviews, and documentation. The population in this research was 338 students, and the sampling technique used was simple random sampling with a sample of 172 students. The data analysis technique used is descriptive statistical analysis using percentages and inferential statistical analysis techniques using data normality tests, product moment correlation analysis, and simple linear regression analysis. Based on the results of the product-moment correlation test analysis, creative product learning and entrepreneurship positively and significantly affect entrepreneurial interest in class XI students majoring in Cosmetology, amounting to 26.7 percent accepted with a low level of influence. The simple linear regression analysis results show the effect of learning creative products and entrepreneurship on student entrepreneurial interest. The results of this research are useful as input that is learning creative products and entrepreneurship based on local potential has the potential to give birth to new entrepreneurs to increase regional economic resilience.
Evaluation of teaching factory using CIPP (Context, Input, Process, Product) model to improve vocational high school students' skills
Authors: Yoto Yoto , Marsono Marsono , Qolik Abdul , Romadin Achmad
Published: March 2024

Teaching Factory (TEFA) is a program that provides a real learning experience for students. Students are taught simulation of job sheet practice, management, marketing, and entrepreneurship. This research examines the feasibility of teaching factories regarding context, input, process, and product. This research uses mixed methods, namely qualitative research supported by descriptive quantitative data. The research was conducted in vocational high schools in East Java Province, Indonesia. The population of this research used a sample size based on the empirical formula from Isaac and Michael. The results showed that: (1) Based on the context, the teaching factory follows the vision and mission of the school and the demand from the community, business, and industry. Production planning achieved a score of 2.825 with a good category, based on the learning questionnaire; (2) Teaching factory learning in VHSs is appropriate in terms of the readiness of buildings and teachers by VHSs, the completeness of facilities, and the readiness of students in attitudes and competencies to get a score of 2.855 with a good category; (3) Based on the process, the learning is appropriate in terms of workshop layout, student activities, product quality control, and practical activities getting a score of 2.825 with a good category; and (4) The evaluation results, concluded that the learning is appropriate in terms of workshop layout, student activities, product quality control, and practical activities getting a score of 2.855 with a good category. The results of this evaluation study can be used to contribute to developing and improving existing programs to make them better.
Development of a hybrid teaching factory model based on school governance in improving employability skills of vocational students
Authors: Wahjusaputri Sintha , Nastiti Tashia Indah , Liu Yingdong
Published: March 2024

This research aims to develop a framework for a hybrid teaching factory model that combines face-to-face and online learning with information and communication technology to improve employability skills in the new normal era. The research method uses the ADDIE Research and Development model, which consists of five stages: analysis, design, development, implementation, and evaluation. The research was conducted in several vocational high schools in DKI Jakarta Region, Indonesia. The findings in the research prove that the implementation of hybrid learning has an impact on students and teachers. Hybrid learning teaching factory is proven to be able to overcome frustrations and limitations between teachers and students in the learning process through online facilities, making teaching factory learning more innovative because there are variations of learning to interact and discuss, and making the classroom atmosphere conducive because students become happy and active in learning and skilled in working. The success of a hybrid teaching factory is considered mutually beneficial for both students and teachers who complete one of the teaching factory learning curriculums while industry instructors develop and work on collaborative platforms that provide useful services such as augmented reality and virtual reality-based applications.
The influence of Teaching Factory (TEFA) implementation and work readiness on vocational high school students' future job perspectives
Authors: Imran Imran , Marji Marji , Suswanto Hary , Adhikari Basanta Prasad
Published: March 2024

Graduates who are superior, competitive, and ready to work are the goals of vocational education. The purpose of this research is to determine the impact of Teaching Factory (TEFA) implementation with the Context, Input, Process, and Product (CIPP) evaluation model and student work readiness on the Occupational Future Time Perspective (OFTP). Indeed, nowadays, professional/vocational graduates must be ready to enter the industrial world after graduation. This research involved 456 respondents of Vocational High School (VHS) students in South Sulawesi, Indonesia, who were determined using a proportional random sampling technique. This type of research is descriptive quantitative with data analysis using SEM AMOS. The results showed that there was a significant influence between the implementation of TEFA and the level of work readiness up to 57%, while the actual contribution of teaching and work readiness factors to future employment time was 80.7%. Overall, the results show that the implementation of TEFA has successfully helped students become more independent and ready to face the competitive world of work. Although 19.3 factors still influence work readiness and prospects, the research results do not change much. Therefore, the application of a teaching factory in the learning process is highly recommended for teachers and students in vocational schools. The implementation can be adjusted to the learning conditions and resources available in VHS. This research contributes as a source of reference in future studies related to improving practical learning and developing students' entrepreneurial skills.
The influence of creativity and learning motivation on entrepreneurial intentions of fisheries vocational high school students
Authors: Priyono Priyono , Agung Achmad Imam , Buditjahjanto I Gusti Putu Asto , Anifah Lilik , Agfianto Tomi
Published: March 2024

The fisheries department is one of the expertise programs in Vocational High Schools (VHS). Learning motivation towards the entrepreneurial intention of VHS students majoring in fisheries is still low. This is supported by the fact that some students do not know the relationship between entrepreneurial intentions and learning motivation, which can arouse students' intentions for entrepreneurship. Therefore, this research aims to see the effect of creativity and learning motivation on the entrepreneurial intentions of VHS students majoring in fisheries. This research is ex post facto research with a quantitative approach because the data is generated in the form of numbers and analyzed based on statistical analysis. The sample in this research was 100 students, and the sampling technique used was purposive sampling. The data analysis technique used is multiple regression analysis. The results of this research explain that creativity has an influence on entrepreneurial intentions, and learning motivation has an influence on entrepreneurial intentions. This is evidenced through the results of the t-test where the sig value obtained is 0.003, whose value is much smaller than 0.05 (0.001 0.05) and the t-count is greater than the t-table (2.614 1.984), so it can be said that creativity has an influence on entrepreneurial intention, as well as the learning motivation variable where the t-count value is greater than the t-table (2.679 1.984). This research contributes to the literature by providing insight into the role of creativity and learning motivation in shaping student entrepreneurial intentions, which can inform the development of entrepreneurship education programs and policies.
BUREAUCRACY REFORM IN TAX SERVICES ONLINE IN THE KUPANG CITY
Authors: Adam Cataryn Vlantyna , Seran Sirilus , Kerihi Anthon Simon Yohanis , Libing Zet Sony
Published: March 2024

This study examined the factors behind the advancement of bureaucratic reform in online tax services at the Regional Technical Service Unit (RTSU) of Regional Income for the City of Kupang, or Unit Pelaksana Teknis Daerah (UPTD). This investigation employed a qualitative method with an interpretive paradigm. This analysis discovered five factors from the internal and external dimensions of the bureaucracy that affect the quality of online tax revenue. From internal sources, this article found that the lack of leadership commitment, the weak capacity and professionalism of implementing actors, and the lack of budget allocations contributed to the weak progress of bureaucratic reform in online tax services. Meanwhile, on the external dimension, this study uncovered that the lack of public pressure and the existence of business practices in the service process contributed to strengthening the low quality of online tax services. To increase the quality of online tax services, the government must ensure that the public managers appointed firmly commit to the bureaucratic reform agenda. Additionally, the government needs to increase front-line implementers' professionalism and capacity to learn information technology. Finally, the government needs to minimize the occurrence of business practices in the process of public services.
USING THE THEORY OF PLANNED BEHAVIOR TO UNDERSTAND PADDY RICE FARMERS' INTENTION TO PARTICIPATE IN WAREHOUSE RECEIPT SYSTEM
Authors: Permana Anggit Jati , Sampurna Agung Firman , Wijaya Chandra , Ulum Bachrul
Published: March 2024

The Warehouse Receipt System (WRS) is one of the tools available for farmers to combat commodity price instability that often happens during harvest season. The WRS Program, complemented by the WRS Subsidy Scheme, has already been implemented in Indonesia for almost fourteen years with minimum participation from the farmers. This research uses a model based on the Theory of Planned Behavior to understand factors affecting the intention level of paddy rice farmers in Demak to participate in the WRS Program. This research employs a quantitative approach, collecting data using surveys to sample farmers from Demak and Dempet Subdistricts and analyzing it using descriptive analysis and SEM-PLS. This research found a small yet positive intention of paddy rice farmers in Demak to participate in the WRS Program. All direct and indirect determinants of intention based on the TPB positively affect farmers' intention to participate in the WRS Program. This research also suggests improving farmers' intention to participate in the WRS Program. The government is advised to build more facilities in the WRS Program warehouse to make it easier for farmers to store their commodities in the WRS warehouses, increasing their perceived behavioral control. The government needs to focus on and increase the frequency of information-sharing sessions regarding the WRS Program to improve farmers' attitudes, subjective norms, and perceived behavioral control. The government can also explore other policy instruments besides the WRS Subsidy Scheme, such as the Nudge to improve farmers' participation In the WRS Program.
EAST JAVA ONLINE SHOPPING AT THE REGIONAL SECRETARIAT OF EAST JAVA PROVINCE
Authors: Satria Ananda Rika , Prathama Ananta
Published: March 2024

Government procurement of goods and services plays an important role in implementation of national development for improving public services and economic development. To achieve that, requires the implementation of E-Government, one of E-Government implementation in procurement of goods and services is JATIM BEJO. The aim of this research is to determine the success implementation of Jawa Timur Belanja Online (JATIM BEJO) in Biro Pengadaan Barang/Jasa Sekretariat Daerah Provinsi Jawa Timur. This study uses a qualitative research method with a descriptive approach. Data collection techniques through interviews, observations, and documentation. The theory used in this research is based on the study and research of the Harvard JFK School of Government concering success factors of E-Government implementation, which consist of support, capacity, and value. The results of the study indicate that the implementation of JATIM BEJO for online procurement of goods and services under 50 million is considered successful because fullfil three success factors that is support, capacity, and value.
ONLINE LEARNING POLICY EVOLUTION STUDY: BIBLIOMETRIC ANALYSIS
Authors: Rethorika Berthanila Rethorika , Yulianti Rachmi , Marthalena Marthalena , Purnama Indra Nizar
Published: March 2024

This study aims to analyze the scientific literature on online learning policies, from the first published paper (2001) to the present (2022) through bibliometric analysis and visualization by utilizing the Scopus database-based VOSviewer software. This study took 384 articles from international authors that focused on the topic of policy online learning. Articles are classified according to the year of publication, author country, co-author, affiliation, keywords and journal title. Furthermore, articles are examined based on several indicators, including the contribution of the state/institution/author. Journal distribution, heavily cited articles, bibliographic coupling and keyword analysis. The search results show that the United States serves as the country of origin of the most prolific author affiliation in publishing articles, the Department of Early Childhood Education, The Education University (Hong Kong) serves as the home of the most prolific author affiliation, and Agaton CB, from The Open University (United States), is considered the most prolific writer. In addition, the Sustainability (Switzerland) Journal is the source of the most discussed publication topics in its journals. Furthermore, Impacts of the Covid-19 Pandemic on Life of Higher Education Students: A Global Perspective is considered the article with the most references. In addition, the most widely discussed topics are online learning policies, e-learning, public education policies, educational policies, sustainable development, digital technologies teaching and learning, mobile learning, blended learning, and distance education.
A SOCIAL RETURN ON INVESTMENT ANALYSIS ON COMMUNITY BASED DISASTER MANAGEMENT TO RESPOND TO PANDEMIC COVID
Authors: Harahap Reiki Nauli , Harlyandra Yorry , Dinariratri Anna Svetla , Putri Mozara Kartika
Published: March 2024

The Covid 19 pandemic has hit various aspects of life in society. Disaster management programs carried out by the government are not fully effective, and community-based disaster management is one solution for this case. One of the problems faced by community-based disaster management is the lack of funding support. Business sector involvement is needed to strengthen community-based disaster management. Inviting multi-stakeholders by involving the community, the business, and the government to provide real solutions to the limitations of disaster programs. This research uses a qualitative and quantitative mixed method with a social return on investment calculation approach to facilitate the process of analyzing the success of community-based disaster management programs. The results of the study stated that the “Program Masyarakat Tanggap Bencana” ASTANA is very feasible to be done and replicated by other stakeholders with an SROI value of 1.60.
The Quality of Education in 50 Countries Reviewed from Sustainable Development Goals: Systematic Literature Review
Authors: Lubis Alya Syafitri , Aryansah Januar Eko
Published: March 2024

Education is an essential aspect of a country. This research aimed to look at countries that implement good quality education and find out what the indicators of the country are in improving the quality of education for sustainable development or see which indicators are used the most in quality education. This research is a systematic literature review. Based on the PRISMA method that has been carried out, a total of 51 journal articles were used in the literature study, and they were obtained from Google Scholar, DOAJ, ScienceDirect, and PubMed. The research results show that indicators of quality education are measured by reliable teaching staff, lifelong education, and completeness of facilities and infrastructure. Of the 51 journal articles examined, 20 journal articles used the indicator of reliable teaching staff as an indicator of education quality, 17 journal articles used lifelong education as an indicator of education quality, and 22 journal articles used indicators of completeness of facilities and infrastructure. These results make completeness of facilities and infrastructure the key to success in creating quality education for sustainable development goals.
Effect of Five Concentrations of Aqueous Extracts of Pleurotus ostreatus P. Kumm and Tagetes minuta
L. on the Mortality of Two Nematodes in a Laboratory Setting.
Authors: Chango Madison , Rosero Gabriela , Erazo Norma , Álvarez Pablo
Published: March 2024

The nematode attacks affect several plant species of Ecuadorian crops. Fungi and plants with nematicide ability are of agricultural interest. This study assessed the effect of five concentrations of aqueous extracts of
Pleurotus ostreatus and Tagetes minuta on the mortality of Meloidogyne spp. and Panagrellus redivivus nematodes in a laboratory setting. The aqueous extracts were obtained through hydro distillation where concentrations of 0%, 0.5%, 5%, 25%, 50% and 100% were prepared. A wholly randomized single-factor design was
used for the P. ostreatus extract and a bifactorial for the T. minuta extract (leaves and flowers). The number
of dead individuals was evaluated, and the efficacy and LC50 were determined. T. minuta leaf extract showcased higher nematicide activity against P. redivivus with an LC50 of 8.03 ppm; when applied to Meloidogyne
sp., the extract showed nematicide activity with an LC50 of 0.01 ppm. For P. ostreatus extract, the greatest
nematicide activity against P. redivivus was an LC50 of 1.22 ppm and nematicide activity against Meloidogyne
sp., was an LC50 of 0.01 ppm. The aqueous extract of T. minuta flowers showed low nematicide activity and
the aqueous extract of T. minuta leaf showed the best nematicide activity.
Keywords: nematicide; Tagetes minuta; Pleurotus ostreatus; Panagrellus redivivus; Meloidogyne sp.
Prevention of cocoa moniliasis using Progressive Web Applications and sensor data in the province of
Francisco de Orellana
Authors: Romero Darwin , Oña Pilar , Aguilar Pedro , Chango Wilson
Published: March 2024

Ecuador is an essential cocoa producer recognized for its quality and aroma. Additionally, it holds a prominent
position among the country's traditional export products, making it the third-largest cocoa-producing country
in the world. However, the cocoa industry faces challenges due to moniliasis, a fungal disease that affects
cocoa trees and causes damage to the fruits, resulting in decreased production. This research aims to prevent
cocoa moniliasis by conducting tests with different algorithms to select the best one for predicting moniliasis
using sensor data in the progressive web application. Various supervised learning algorithms were applied,
including PCA, IPCA, KPCA, Linear Regression, Sci-Kit Learning, and ensemble methods like Bagging and
Boosting. Google's Lighthouse is utilized for artifact validation. It is concluded that the Boosting ensemble
method with a value of 1.0 and 4 estimators is the algorithm that shows a good fit for prediction. In artifact
validation, it yields favorable results with a score of over 90 in various Lighthouse parameters.
Keywords: Moniliasis 1; Progressive Web Application 2; PCA 3; IPCA 4; KPCA 5; Linear Regression 6;
Bagging 7; Boosting 8; Lighthouse 9
Cerebral Microbleeds in Different Brain Regions and Their Associations With the Digital Clock-Drawing Test: Secondary Analysis of the Framingham Heart Study
Authors: Akhter-Khan Samia C , Tao Qiushan , Ang Ting Fang Alvin , Karjadi Cody , Itchapurapu Indira Swetha , Libon David J , Alosco Michael , Mez Jesse , Qiu Wei Qiao , Au Rhoda
Published: March 2024

Background

Cerebral microbleeds (CMB) increase the risk for Alzheimer disease. Current neuroimaging methods that are used to detect CMB are costly and not always accessible.

Objective

This study aimed to explore whether the digital clock-drawing test (DCT) may provide a behavioral indicator of CMB.

Methods

In this study, we analyzed data from participants in the Framingham Heart Study offspring cohort who underwent both brain magnetic resonance imaging scans (Siemens 1.5T, Siemens Healthcare Private Limited; T2*-GRE weighted sequences) for CMB diagnosis and the DCT as a predictor. Additionally, paper-based clock-drawing tests were also collected during the DCT. Individuals with a history of dementia or stroke were excluded. Robust multivariable linear regression models were used to examine the association between DCT facet scores with CMB prevalence, adjusting for relevant covariates. Receiver operating characteristic (ROC) curve analyses were used to evaluate DCT facet scores as predictors of CMB prevalence. Sensitivity analyses were conducted by further including participants with stroke and dementia.

Results

The study sample consisted of 1020 (n=585, 57.35% female) individuals aged 45 years and older (mean 72, SD 7.9 years). Among them, 64 (6.27%) participants exhibited CMB, comprising 46 with lobar-only, 11 with deep-only, and 7 with mixed (lobar+deep) CMB. Individuals with CMB tended to be older and had a higher prevalence of mild cognitive impairment and higher white matter hyperintensities compared to those without CMB (P<.05). While CMB were not associated with the paper-based clock-drawing test, participants with CMB had a lower overall DCT score (CMB: mean 68, SD 23 vs non-CMB: mean 76, SD 20; P=.009) in the univariate comparison. In the robust multiple regression model adjusted for covariates, deep CMB were significantly associated with lower scores on the drawing efficiency (β=–0.65, 95% CI –1.15 to –0.15; P=.01) and simple motor (β=–0.86, 95% CI –1.43 to –0.30; P=.003) domains of the command DCT. In the ROC curve analysis, DCT facets discriminated between no CMB and the CMB subtypes. The area under the ROC curve was 0.76 (95% CI 0.69-0.83) for lobar CMB, 0.88 (95% CI 0.78-0.98) for deep CMB, and 0.98 (95% CI 0.96-1.00) for mixed CMB, where the area under the ROC curve value nearing 1 indicated an accurate model.

Conclusions

The study indicates a significant association between CMB, especially deep and mixed types, and reduced performance in drawing efficiency and motor skills as assessed by the DCT. This highlights the potential of the DCT for early detection of CMB and their subtypes, providing a reliable alternative for cognitive assessment and making it a valuable tool for primary care screening before neuroimaging referral.
Near Real-Time Syndromic Surveillance of Emergency Department Triage Texts Using Natural Language Processing: Case Study in Febrile Convulsion Detection
Authors: Khademi Sedigh , Palmer Christopher , Javed Muhammad , Dimaguila Gerardo Luis , Clothier Hazel , Buttery Jim , Black Jim
Published: March 2024

Background

Collecting information on adverse events following immunization from as many sources as possible is critical for promptly identifying potential safety concerns and taking appropriate actions. Febrile convulsions are recognized as an important potential reaction to vaccination in children aged <6 years.

Objective

The primary aim of this study was to evaluate the performance of natural language processing techniques and machine learning (ML) models for the rapid detection of febrile convulsion presentations in emergency departments (EDs), especially with respect to the minimum training data requirements to obtain optimum model performance. In addition, we examined the deployment requirements for a ML model to perform real-time monitoring of ED triage notes.

Methods

We developed a pattern matching approach as a baseline and evaluated ML models for the classification of febrile convulsions in ED triage notes to determine both their training requirements and their effectiveness in detecting febrile convulsions. We measured their performance during training and then compared the deployed models’ result on new incoming ED data.

Results

Although the best standard neural networks had acceptable performance and were low-resource models, transformer-based models outperformed them substantially, justifying their ongoing deployment.

Conclusions

Using natural language processing, particularly with the use of large language models, offers significant advantages in syndromic surveillance. Large language models make highly effective classifiers, and their text generation capacity can be used to enhance the quality and diversity of training data.
Predictors of response to a single session intervention to reduce emotional distress using of Enhanced Psychoeducation among professionals from essential services during the COVID-19 pandemic (Preprint)
Authors: Ache Ana , Montezano Bruno Braga , Mosqueiro Bruno Paz , Caldieraro Marco Antonio , Spanemberg Lucas , Salum Giovanni , Fleck Marcelo P
Published: March 2024

BACKGROUND

Single session intervention (SSI) have been shown to be an effective strategy for reducing emotional distress. Enhanced psychoeducation (EP), which includes empathic listening, risk stratification, symptom monitoring, and habit modification, is particularly suitable for SSIs.

OBJECTIVE

This study aimed to investigate the predictors of responses to an online EP intervention among professionals from essential services during the COVID19 in Brazil with the aim of reducing emotional distress.

METHODS

This study analyzed data from the TELEPSI Project from April 2020 to December 2021. We included all participants who received SSIs with high levels of emotional distress (T-scores above 70 on the PROMIS for anxiety, depression, or irritability).

RESULTS

The final sample consisted of 460 subjects, 89.1% female and 81.1% health professionals, of which 300 subjects underwent reassessment in one month (65% retention). Participants with harmful use of social networks, spending time on social media, playing video games, smoking, drinking alcohol, using drugs, and spending time with pets showed a less pronounced decrease in symptoms. In contrast, participants who played instruments or had already received psychological treatment showed a greater magnitude of decrease in symptoms.

CONCLUSIONS

This study highlights the impact of lifestyle factors on SSI efficacy. These results underscore the importance of considering individual lifestyle factors when implementing SSIs, and contribute to a growing body of evidence supporting the tailored application of psychoeducational strategies in mental health interventions, particularly in high-stress environments.
Thinking, a Diverse and Inclusive Process—An Epistemological Look
Authors: Salum Tome, Ph.D Jose Manuel
Published: March 2024

Contemporary education has taken on the challenge of promoting different programs aimed at promoting inclusive teaching-learning processes that facilitate attention to diversity. It is evident that the integration of students with special needs in regular educational centers has caused significant changes in the curriculum, infrastructure and training among teachers. In the last ten years, educational inclusion has made significant progress, but much remains to be done to expand inclusive spaces.
Exploration on the Transformation of Retail Finance Marketing from “Sitting Business” to “Traveling Business” in Commercial Banks —Take the “Pop-up Shop” Model of C Bank Pedestrian Street as an Example
Authors: Yuqi Deng
Published: March 2024

“Sitting business” and “traveling business” are two commonly used modes in marketing practice. “Sitting business” refers to waiting for customers to come to the door, is a kind of signboard and business name to attract customers of the marketing model, such as the commercial street store model, its business characteristics and competitiveness are mainly reflected in the attractiveness of products and the quality of service in the hall. “Merchant” refers to proactive marketing customers, and now refers more to product promotion and branding based on planning and packaging, as well as marketing personnel to communicate and interact with customers outside the business premises to expand product influence and market share. It is generally believed that sitting merchants are a conventional, traditional and inefficient marketing model compared to merchants. In the operation of modern enterprises, due to the increasingly fierce market competition, the transformation from “sitting business” to “traveling business” has become the consensus of business managers, and the awareness and ability of business are gradually becoming the core competitiveness of modern enterprise marketing.The operation mode of traditional bank branches is more of a passive waiting for customers to come to the door and accept the business needs of customers, reflecting the concept of “sitting in business”. With the sudden rise of Internet finance, which has had a huge impact on traditional banking business, the banking industry has realized that there may be a “last mile” shortcoming in physical outlets in financial services, and narrowing the distance between bank outlets and customers is more about “going out”, such as developing community banks, carrying out door-to-door marketing, expanding business districts, etc., mining customer needs outside bank outlets, building “scenario-based” finance, and constantly trying to change the business model from “sitting business” to “ traveling business”.
Studying the Effect of Elastoviscoplastic Modeling on Burst Speed Margins of Turbine Disc
Authors: Swamy Bheesetti Haresh
Published: March 2024

Abstract: Aero engine turbine discs are critical components that can shatter owing to excessive mechanical loads during operation, endangering aircraft safety. Understanding burst speed margins, the buffer between maximum and actual rotational speeds, is essential for ensuring safety. ANSYS APDL software is used to examine a 2D axisymmetric non-uniform turbine disc utilizing INCONEL 718 and Ti-6Al-4V materials and numerous models. This research, which includes centrifugal, gas, and thermal loads, accurately predicts burst speed and confirms linear elastic analysis. For elastic-plastic analysis, Robinson's criterion is utilized, Perzyna for elastoviscoplastic behavior, and Bilinear isotropic hardening for elastic-plastic behavior. A sensitivity analysis of Perzyna model parameters reveals that elastoviscoplastic analysis yields higher burst margins than elasticplastic analysis for this turbine disc. The parameters are calibrated using existing models from the Literature.
Industrial Internet of Things (IIOT)
Authors: Dangat Prof. M. T.
Published: March 2024

Abstract: Industrial Internet of Things (IOT) is a part of IOT (Internet of Things) technology that has provided an opportunity to build powerful industrial system and applications by using different I0T devices like sensors, actuators, Resistance Temperature Detectors, pH analyzers, Level Transmitters, RFIDs, wireless and mobile devices. High-speed internet has given a new hike in development of IOT devices, as it makes remote access facility at ease. Various IOT applications and systems have been developed and deployed in recent years. To understand the development of IOТ technology, this study addresses IIOT concepts through a systematic review of various white papers, research papers and online database.Abstract: Industrial Internet of Things (IOT) is a part of IOT (Internet of Things) technology that has provided an opportunity to build powerful industrial system and applications by using different I0T devices like sensors, actuators, Resistance Temperature Detectors, pH analyzers, Level Transmitters, RFIDs, wireless and mobile devices. High-speed internet has given a new hike in development of IOT devices, as it makes remote access facility at ease. Various IOT applications and systems have been developed and deployed in recent years. To understand the development of IOТ technology, this study addresses IIOT concepts through a systematic review of various white papers, research papers and online database.
Smart Shopping Trolley Using Internet of Thing (IOT)
Authors: Dangat Prof. M.T.
Published: March 2024

Abstract: Currently, billions of consumers shop at large in shopping mall stores for their everyday necessities. In today's life, purchasing has become a part of everyday life. On holidays and weekends, malls experience massive crowds .The integration of Internet of Things (IoT) technology into shopping trolleys presents a significant advancement in the retail industry, revolutionizing the shopping experience for consumers. The proposed SST utilizes a network of sensors and connectivity solutions to enable real-time monitoring, inventory management, and personalized services. Through IoT integration, the SST offers features such as automatic item detection, seamless checkout processes, and personalized recommendations based on individual shopping habits. Moreover, the system empowers retailers with tools for efficient inventory management, reducing stock outs, and analysing consumer behaviour for targeted marketing strategies. Intelligent Carts and automated recognition systems are being utilized by merchants to enhance the efficiency of their offerings and to boost market demand, enabling them to save time and money.
Tax Evasion Detection: A Fusion of AdaBoost Classifiers and Deep Learning Models
Authors: Sailaja Ms. Manyala Naga
Published: March 2024

Abstract: Income Tax plays an important role in the developmental activities of a nation. However some companies try to avoid or minimize their tax by showing reduced profits. Such companies can be identified by studying and analyzing the balance sheet indicators like: Sale Volume Vs. Purchase Volume, Employee Growth Vs. Taxpaid, Total Sales Vs. Taxpaid, Claims Crediting, Net Income Vs. Tax Increase, Gross Loss Vs. Tax, Inventories Vs. Direct Income, Gross Revenue Vs. Taxpaid, Raw Material Purchase Cost (RMPC) Vs. Final Product Sales (FPS) and Fixed Assets Vs. Taxpaid. This project aims at Tax Evaders detection by using a dataset, containing the above mentioned identification factors in the balance sheet. The analysis is carried out by using AdaBoost classifier and also by using a Deep learning model
HAVAE – An Advanced Approach for Malware Detection Using Deep Learning
Authors: Kiran Vadduri Uday
Published: March 2024

Abstract: The dynamic situation of cybersecurity necessitates continuous adaptation to the evolving and sophisticated nature of malware. This study proposes an innovative approach to enhancing threat detection methodologies by combining Adversarial Autoencoders (AAEs) and Variational Autoencoders (VAEs) for unsupervised malware detection. AAEs, with their EncoderDecoder structure and adversarial techniques, are integrated with VAEs to discern latent representations which are crucial for discriminating between malware and harmless software. This model, referred to as Hybrid Adversarial-Variational Autoencoder (HAVAE), takes advantage of both of their strengths architectures, capturing nuanced features within a latent space through unsupervised learning. The HAVAE model employs the Reparameterization Technique, crucial for sampling latent variables, ensuring the generation of realistic samples while retaining discriminative attributes essential for accurate malware identification. Through comprehensive evaluations across diverse datasets, the efficiency of HAVAE is assessed using metrics encompassing precision, recall, and F1-score. The evaluation underscores the model's robust ability to detect malicious software effectively, emphasizing its potential as a versatile cybersecurity tool. This innovative approach represents a revolution in cybersecurity, utilizing the strength of unsupervised learning techniques, AAEs, and VAEs. The findings signify a significant advancement in adaptive and resilient malware detection systems, illuminating pathways for improved threat identification and mitigation in the ever-evolving cybersecurity landscape
A Novel Approach on Early Prediction of Employee Attrition in an Institution using Machine Learning Techniques
Authors: J Padmapriya
Published: March 2024

Abstract: Institutions can only succeed if they have good employees. Retaining a good employee in an institution is a must to its growth. Sometimes, employees face issues in their institution because of overwork, no promotions, no rewards for good work, not seeing eye to eye with their manager, frequent business trips and extreme conditions which lead them to look for new jobs in the market. Employee attrition can be curbed if these causes are found sooner. To predict an employee’s resignation, Machine Learning Techniques are utilized. Attrition rates in an organization are predicted by factors such as work-life balance, opportunities, office atmosphere, pay, and other benefits. The Human Resources team will find the attrition rate data to be quite helpful in keeping exceptional employees. Random Forest, K-Nearest, Support Vector Machine and XG Boost are algorithms used to predict the attrition rate in an institution. The Human Resources Management (HRM) dataset is used by the models to detect various data aspects and efficiently estimate employee attrition.
A Study on the Challenges and Opportunities of Women Entrepreneurs
Authors: Mistry Faaz
Published: March 2024

Abstract: This research explores the complex terrain of female entrepreneurship with the goal of illuminating the diverse obstacles and prospects encountered by female entrepreneurs. Gender inequality still prevents women from fully participating in and succeeding in the business sphere, even in spite of the increasing recognition of their contributions to the entrepreneurial ecosystem. This study clarifies the structural, cultural, and institutional obstacles that face female entrepreneurs by an extensive analysis of the body of literature and also with the help interviews of women entrepreneurs in-person and through google forms the data that was collected was then analysed. This research adds to a deeper understanding of the complex factors influencing women's entrepreneurial aspirations and provides insights into practical solutions for creating gender-inclusive entrepreneurship ecosystems by critically assessing both possibilities and constraints.
Advancements in Emotion Detection: A Comprehensive Review of Text and Audio-Based Approaches
Authors: K Arjun Raj
Published: March 2024

Abstract: Emotion detection from text and audio has garnered significant attention in recent years due to its applications in various domains such as mental health support, conversational agents, and stress detection. In this review, we analyze methodologies and results in this field. The papers cover a range of topics including machine learning models for emotion detection from speech, deep learning methods for text-based emotion classification, and the integration of AI in healthcare and education. Traditional machine learning algorithms such as logistic regression and SVM are compared with deep learning architectures like CNNs and RNNs/LSTMs for their effectiveness in emotion detection tasks. Spectral and acoustic features for speech emotion detection are examined alongside text-based emotion detection approaches utilizing lexicon-based methods and machine learning techniques. Furthermore, the impact of voice assistant personality on user attitudes and behavior is explored, shedding light on the importance of designing emotionally intelligent AI systems. Additionally, challenges and ethical considerations in deploying AI-driven solutions in mental health and healthcare settings are discussed. Through this comprehensive review, we provide insights into the current state of emotion detection research, highlighting trends, challenges, and future directions in the field
Impact of AI in the Animation Industry
Authors: Pandit Abhishek R.
Published: March 2024

Abstract: This manuscript develops into the transformative role of artificial intelligence (AI) in the field of animation. Over the years, AI has revolutionized various industries including healthcare, banking, e-commerce, agriculture, manufacturing, and others., and animation is no exception. This paper provides an overview of the applications of AI in animation, including character creation, motion synthesis, scene generation, generating facial expressions and body language for characters, planning out scene layouts and camera movements, and generating backgrounds and environments and more. It discusses the challenges and ethical considerations associated with AI-driven animation and presents insights into the future directions of AI in the animation industry
Enhancing Credit Card Fraud Detection: A Novel Approach with Random Forest and Behavioral Biometrics
Authors: Adapa Srinivasa Rao
Published: March 2024

Abstract: In an era marked by heightened fraudulent activities targeting credit card transactions, this research delves into the efficacy of advanced machine learning algorithms in combating such threats. The performance of logistic regression, decision trees, and the novel random forest approach in identifying fraudulent transactions is scrutinized. Additionally, behavioral biometrics are incorporated as an innovative authentication factor to enhance fraud detection accuracy. Incorporating behavioral biometrics as an additional authentication factor represents a cutting-edge approach to fraud detection, leveraging the unique behavioral patterns exhibited by individuals during transactional interactions.
Enhancing Collaboration and Efficiency through Automated Documentation of GCP Stored Procedures with genAI
Authors: S Srilakshmi
Published: March 2024

Abstract: In the realm of cloud-based data management on the Google Cloud Platform (GCP), effective documentation of work processes is essential for streamlined operations. However, existing practices often rely on manual efforts, leading to incomplete and time-consuming documentation. To overcome this challenge, we propose a Lab Week project leveraging genAI's natural language processing capabilities. Our objective is to develop a Python script that automates the generation of comprehensive documentation for GCP Stored Procedures. This innovative solution aims to extract crucial details such as Common Table Expressions (CTEs), temporary tables, end tables, and associated columns, resulting in a structured Word document. By harnessing genAI's power, our automated documentation system not only saves time but also ensures consistency and depth in GCP Design Details. This initiative fosters collaboration and knowledge transfer among team members, with the generated Word documents seamlessly integrating into Confluence pages as valuable resources for our development teams
Visual Question Generation from Remote Sensing Images Using Gemini API
Authors: Kamala M.
Published: March 2024

Abstract: Visual Question Generation Extracting Information from Remote Sensing Images Remote Sensing Images plays a vital role in understanding and extracting information from aerial and satellite images. Utilizing Bidirectional Encoder Representation from Transformers (BERT) for extracting valuable insights from remote sensing images. Gemini Application Programming Interface(API), and Convolution Neural Networks (CNNs) are used. First, The proposed methodology employs CNN to extract high-level features from remote sensing images, capturing spatial data and generatingquestions. Similarly, the Gemini Application Programming Interface(API) integrates contextual understanding into the question-generation process by providing relevant environmental data. Lastly, BERT functions as a language model in which employees enhance and refine the generated questions by taking into account both the syntax and semantics. Hence, by combining all these techniques we are capable of generating required relevant questions from remote sensing images in an enhanced and efficient way.
Education and observation of the 2023 hybrid solar eclipse in Southwest Papua
Authors: Raharja Endra Putra , Faizin Muhammad , Pramudya Yudhiakto , Nurinaya N. , Triono Muhklas , Raharja Surya Putra
Published: March 2024

On April 20, 2023, the Southwest Papua region was the areas crossed by a hybrid solar eclipse. This outreach aims to enhance the community's understanding and awareness regarding the safety of solar eclipse observation by conducting observations at several locations in Southwest Papua. The Solar Gehana Education and Observation Program was successfully implemented in 6 Regencies/Cities in Southwest Papua. This activity was carried out using mini-lectures, pinhole training, and joint eclipse observations. In general, this program can increase public understanding and awareness regarding the safety of observing solar eclipses, with the training participants' response score being 79.92% in the good category. Apart from that, this program has succeeded in touching and opening people's insight into this fascinating natural phenomenon through widespread publication in the mass media, both regional and national.
Student metacognition in online learning using website hyperlink facilities on Microsoft Team’s platform
Authors: Karyanto Puguh , Rohman Aris Nur
Published: March 2024

Without the ability to analyze their own learning (metacognition), students can find it difficult to manage their educational journey. The good news is, future learning environments on online platforms offer innovative ways to develop these skills, even for those who are digital natives. This study aims to ascertain the effectiveness of online learning by using a website hyperlink-based teaching and learning scenario, especially using the Microsoft Teams platform towards students’ metacognition during online learning. Both metacognition knowledge and awareness (regulation), representing the metacognition, were checked. Coordination systems in learning biology were the chosen topics to be taught in quasi-experimental research using a pretest-posttest nonequivalent control group design. Two classes were selected purposely based on the normality and homogeneity test to have the controlled and experimental classes. We examined the metacognitive knowledge and awareness using tests and questionnaires. We checked the effectiveness of the web hyperlink-based media on students’ metacognition knowledge and awareness using MANOVA Regression analysis and N-Gain scores. Our developed web hyperlink-based media has significantly enhanced students’ cognitive knowledge and awareness.