bluuebunny/crossref_metadata_2025 huggingface dataset:
Potential Analysis of Drug Interactions in Schizophrenia Cases At One of the Bandung District Pharmacy, Indonesia
Authors: Herliany Yane Srie , Nurdin Hari
Published: March 2024

Schizophrenia is a heterogeneous syndrome of thought disturbances, delusions, hallucinations, emotional disturbances, decreased cognitive function and disturbed psychosocial functions. Treatment of schizophrenia aims to prevent hospitalization, ensure the patient's symptoms are stable and independent such as work or school. The purpose of this study was to determine the potential for drug interactions in schizophrenia cases based on their mechanism of action and severity. This research method is a descriptive study using data collected retrospectively on prescription drug data sheets in the pharmacy. The collected data were analyzed quantitatively and qualitatively. The results of the study from a total of 133 prescriptions found potential drug interactions in 509 cases. Drug interactions based on the mechanism of action, pharmacodynamics were found in 494 cases and 15 cases of pharmacokinetic . Based on the level of severity, there were interactions with 15 cases of mild severity, 287 cases of moderate severity and 217 cases of severe severity
Pengelolaan SDM Pengawas Ketenagakerjaan
Authors:   Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan, Tobing Henriko , Hazami Muhammad Fikrie ,   Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan, Nasution Faizal Amir Parlindungan ,   Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan, Muhyiddin Muhyiddin ,   Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan
Published: March 2024

Pelaksanaan pengawasan ketenagakerjaan masih belum berjalan efektif, hal ini diindikasikan dengan lemahnya unsur-unsur dalam sistem pengawasan ketenagakerjaan salah satunya SDM, hingga kini isu SDM tidak pernah beranjak dari permasalahan terkait keterbatasan jumlah. Untuk menyelesaikan permasalahan ini, terdapat dua alternatif kebijakan yang dapat dilakukan apabila merujuk pada amanat UU nomor 23 tahun 2014 tentang Pemerintahan Daerah yaitu: 1) pengelolaan SDM Pengawas Ketenagakerjaan dikelola secara terpusat (sentralisasi penuh), 2) pengelolaan SDM pengawasan tetap berada di Provinsi (sentralisasi terbatas) dimana kedua pilihan tersebut memiliki tantangan masing-masing. Kajian ini bertujuan untuk mengetahui sistem pelaksanaan pengelolaan SDM pengawas ketenagakerjaan (sentralisasi terbatas) sebagai bahan masukan kebijakan atas kedua pilihan tersebut. Kajian ini juga memberikan gambaran dari beberapa unsur lainnya dalam sistem pengawasan ketenagakerjaan yaitu metode/prosedur, sarana prasarana dan anggaran. Kajian ini dilakukan dengan menggunakan pendekatan kualitatif dan kuantitatif untuk mendapatkan gambaran yang lebih utuh terkait pelaksanaan pengawasan ketenagakerjaan khususnya pengelolaan SDM. Hasil pengolahan data menunjukkan bahwa beberapa hal yang perlu diperhatikan terkait pemindahan pengelolaan SDM adalah persoalan remunerasi dan penugasan (tour of duty). Dari sisi administrasi, persoalan penyelesaian aturan pengalihan SDM menjadi hal krusial yang harus diselesaikan. Terkait sistem pengawasan secara umum dalam model sentralisasi terbatas, metode atau prosedur pengawasan ketenagakerjaan dimana diantaranya terkait dukungan penyelesaian kasus serta kultur pejabat pengawas yang mengedepankan penyelesaian non yustisi dianggap kurang efektif. Sementara permasalahan klasik seperti keterbatasan jumlah SDM, anggaran dan sarana prasarana masih menjadi kendala umum yang terjadi di lapangan. Sistem pengawasan sebaiknya berada di pemerintah pusat.
Evaluasi Partisipatif Perluasan Kesempatan Kerja Berbasis Kawasan
Authors:   Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan, Yanwar Yanwar , Kolopaking Lala M. , Tonny Fredian , Muhyiddin Muhyiddin ,   Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan
Published: March 2024

Persoalan pokok bidang ketenagakerjaan adalah kelebihan tenaga kerja serta kecilnya kesempatan kerja yang tercipta pada setiap sektor sehingga banyak terjadi pengangguran. Kebijakan Ketenagakerjaan yang diambil lebih mengutamakan tenaga kerja formal dan industro di perkotaan. Perluasan kesempatan kerja dan usaha berbasis kawasan sebagai salah satu terobosan. Keputusan Menteri Ketenagakerjaan Republik Indonesia Nomor 38 Tahun 2022 tentang 9 Lompatan Kementerian Ketenagakerjaan, khususnya dalam pelaksanaan Lompatan Ketiga tentang Transformasi Program Perluasan Kesempatan Kerja membuka ruang baru dalam melakukan perluasan kesempatan kerja. Kementerian Ketenagakerjaan telah berhasil menginisiasi 20 kawasan produktif yang mencakup Indonesia Barat, Tengah, dan Timur. Kajian ini bertujuan untuk mengukur efektivitas penyelenggaraan Perluasan Kesempatan Kerja Berbasis Kawasan dalam kerangka membangun ekosistem ketenagakerjaan yang inklusif dan kolaboratif. Pengukuran efektivitas penyelenggaraan Perluasan Kesempatan Kerja Berbasis Kawasan menggunakan metodologi Importance Performance Analysis (IPA). Pengambilan data yang dilakukan, terdiri dari 5 dimensi yaitu kondisi profil, ekonomi, sosial, lingkungan (sumberdaya alam), dan tata kelola. Berdasarkan hasil awal analisis kategori dan peringkat kemajuan lokasi PKK berbasis kawasan, teridentifkasi ke-20 kawasan tersebar di 4 tipe kawasan dalam kuadran kartesius kemajuan kawasan yakni 1) Kawasan siap berkembang; 2) Kawasan berpotensi berkembang; 3) Kawasan perlu usaha besar untuk berkembang; dan 4) Kawasan dengan perkembangan terbatas. Program PKK berbasis Kawasan memerlukan mekanisme pendampingan yang saling memberdayakan dalam rangka pemberdayaan sosial. Penguatan kapasitas sumberdaya manusia di lintas desa dalam satuan Kawasan yang didukung dengan revitalisasi sarana dan prasarana. Ekosistem program ketenagakerjaan yang perlu dibangun seperti padat karya, TKM pemula, pelatihan, TKM lanjutan, pelatihan dan pendampingan lanjutan dengan kolaborasi multisektor. Rekomendasi yang diberikan adalah membuat program Festival Digital.
Percentile Rankings And Normalization Of Performance For International Weightlifting Federation World Championships Competitors
Authors: Gillen Zachary
Published: March 2024

The present study provided percentile rankings and gender and weight class comparisons, for absolute and allometrically scaled Olympic Weightlifting (OW) performance, as well as Sinclair scores, for International Weightlifting Federation (IWF) World Championships competitors. Data from the IWF Senior World Championships for 2018, 2019, 2021, and 2022 were compiled. OW performance was allometrically scaled for body weight, and Sinclair scores were calculated based on IWF standards. Percentile rankings and gender and weight class differences were computed for absolute and allometrically scaled OW performance measures, as well as Sinclair scores. This study provided percentile rank values for absolute, allometrically scaled, and Sinclair scored OW performance with the weight classes implemented in 2018 by the IWF. OW performance increased with weight class. When allometrically scaled, the lightest and heaviest weight classes had the lowest OW performance, while Sinclair scores were similar across weight class. Men remained stronger than women for allometrically scaled clean & jerk and total, which was similar for gender-related comparisons of Sinclair scores, while allometrically scaled snatch was similar for men and women. The present results provide coaches and practitioners percentile rankings and allometric parameters that may be used to assess OW performance for elite-level weightlifting competitors.
Retos Socioeconómicos de la actualidad en el contexto global
Authors:   Universidad Católica Luis Amigó, Chirinos Araque Yamarú del Valle , Rojas Nieves Dorkys Coromoto ,   Universidad Nacional Experimental Rafael María Baralt, Barbera Alvarado Nataliya ,   Universidad del Sinú-Elías Bechara Zainum
Published: March 2024

El libro “Retos socioeconómicos de la actualidad en el contexto global”, es resultado del III Encuentro Internacional de Investigación, organizado por la Alianza de Investigadores Internacionales ALININ, donde participaron investigadores, académicos y científicos presentando reflexiones acerca del fenómeno desde distintas áreas del conocimiento, con una mirada inter y transdisciplinaria de la ciencia. En tal sentido, este libro representa un medio científico que permite la difusión y transmisión de conocimientos significativos para la comprensión de la situación desde diversos puntos de vista.
The impact of cultural diversity and intercultural competence on conflict management styles in Egypt
Authors: Said Ali Ahmed
Published: March 2024

Purpose: To aim of the study was to analyze the impact of cultural diversity and intercultural competence on conflict management styles in Egypt.

Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.

Findings: Research shows power dynamics and organizational justice significantly impact conflict outcomes and satisfaction. Power imbalances exacerbate conflicts, leading to dissatisfaction. Unequal power distribution can escalate conflicts due to perceived unfairness. Conversely, organizational justice fosters positive outcomes and higher satisfaction levels. Fostering equitable power dynamics and promoting justice mitigate conflict and enhance satisfaction.

Unique Contribution to Theory, Practice and Policy: Cultural intelligence theory, social identity theory & intercultural communication competence (ICC) theory may be used to anchor future studies on the impact of cultural diversity and intercultural competence on conflict management styles in Egypt. Provide training and development programs focused on enhancing intercultural competence among individuals and organizations. Advocate for the implementation of diversity and inclusion policies that promote cultural sensitivity and intercultural competence in the workplace.
Women and Rural Development in the Context of Decentralisation in Tubah Council, North West Region of Cameroon
Authors: Temgoua Nadine Yemelong , Bouyo Jim Narem Kwin , Mofor Gilbert Zechia , Nahbila Mearan Forchop
Published: March 2024

Purpose: Before 1990s, the decentralisation process in Cameroon has gone through several stages. This process really materialised with the revision of the Cameroon Constitution in 1996. Owing to that, several laws concerning this process were promulgated. Decentralisation should eventually make all decentralised territorial authorities autonomous. This government policy, which is part of the bottom-up approach, has the responsibility to promote the participation of the population. Thus, one is entitled to ask how decentralisation in Cameroon can impact women’s participation in rural development in the Tubah council?

Materials and Methods: To answer this question, primary and secondary data were mobilised and processed according to appropriate methods for the variables studied. Primary data were collected from observation, interviews of key informants and administration of questionnaires addressed to a sample of 145 people in the council area. Secondary data were obtained from internet sources, textbooks as well as documentations and libraries. Quantitative data were analysed using descriptives and inferentials statistics meanwhile content analysis was used for qualitative data analysis.

Findings: The results show that even if the decentralisation process does not pay particular attention to women, it contributes to the creation of a favourable framework for local development initiatives. Thus, 79% of the women in the Tubah area act individually, compared with 21% who take part in group actions. Their actions varied, with 40% in the field of agriculture and 30% to drinking water supply projects. Identified as one of the pillars of development in rural areas, the intervention of women, if well supervised and structured, it can boost development in a sustainable and effective way.

Implications to Theory, Practice and Policy: The study recommends that councils within the process of decentralisation should encourage women participation in rural development by ensuring their high representativeness. It also goes in line with the theory of decentralisation, which states that in order to promote local and participatory development, the transfer of power to local authorities must be effective. The study emphasises on the fact that, the transfer should not only be at the level of power and finances, but it should also include the social categories, must especially women, who are one of the main actors of rural development in Cameroon.
Ta’limning samaradorligini ta’minlashda oila va maktab hamkorligining ahamiyati
Authors: Matkarimov Akramjon
Published: March 2024

Maqolada xalqaro baholash tadqiqotlarida oila va ta’lim muassasasi hamkorligining ijtimoiy-pedagogik jihatlari yoritilgan. O‘quvchining ta’limda muvaffaqiyatga erishishida oila muhiti, oiladagi ta’lim resusrslarining shakllanganligi hamda ota-onalarning pedagogik kompetentsiyalari hal qiluvchi ahamiyatga ekanligi ilmiy manbalar asosida tahlil qilingan. Xalqaro baholash tadqiqotlarida ham oila va ta’lim muassasasi hamkorligining ijtimoiy-pedagogik jihatlariga keng e‘tibor qaratilgan. Maqolada shuningdek oila va maktab hamkorligini yo‘lga qo‘yishdagi muammolar ham tizimli tahlil qilingan.
Paralimpiya va para o‘yinlar tarixiga bir nazar yohud O‘zbekistonning paralimpiadadagi o‘rni
Authors: Hamidova Shoira
Published: March 2024

Mazkur maqolada paralimpiya tarixiga doir mulohazalar, parasportning o‘ziga xos jihatlari, shu bilan birgalikda o‘zbek paralimpiyasidagi ilk qadamlar, dastlabki g‘alabalar va bugungi kunga qadar sohada olib borilayotgan islohotlar xususida so‘z yuritiladi.
Impact of Capital Market Development on Industrial Sector Output in Nigeria: 1986-2022
Authors: Aiyedogbon John O. , Abayomi Awujola , Okoroike Ngozi J.
Published: March 2024

The ideal situation for any growing economy, especially one as diversely poised as Nigeria, is to have a thriving industrial sector. This sector is traditionally seen as an indispensable engine of growth, a crucial antidote to the challenges of unemployment, and a potent creator of wealth. Regrettably, the current state of the manufacturing sector in Nigeria falls significantly short of this ideal. Recent statistics indicate that the industrial sector’s contribution to GDP remains suboptimal, with the National Bureau of Statistics (NBS) reporting a decline in industrial growth despite several policy measures aimed at enhancing its growth. This research sought to investigate the impact of capital market development on the growth of industrial sector in Nigeria between 1986 and 2022 with the aid of ARDL estimation technique. The findings revealed that Stock Market Capitalization had a significant and positive impact on industrial sector growth, suggesting that as market capitalization increased, the industrial sector flourished. In the same vein, Turnover Ratio, which is the value of traded shares divided by market capitalization, was found to enhance industrial sector growth positively and significantly. These positive relationships underscored the pivotal role the capital market plays in boosting the industrial sector by mobilizing and channeling resources efficiently. However, the All-Share Index exhibited a negative relationship with the industrial sector growth, indicating that while general market sentiment might be positive, it might not always translate to growth in the industrial sector. Based on these findings, the paper recommended promoting public listings and private investments as a strategy to enhance the growth potential of the industrial sector. Furthermore, improving trading platforms, ensuring market transparency, and reducing transactional costs were emphasized to leverage the benefits of a high turnover ratio. For the negative aspects found with All-Share Index, policymakers were advised to address these components to ensure holistic development of both the capital market and the industrial sector.
Acceptability of Peer Networking Strategy in Combating Substance Abuse Among Undergraduates in Universities in Anambra State
Authors: Onuchukwu Uju Cecilia , Onuchukwu Obini Ijeoma
Published: March 2024

Substance abuse among the youths has been on the increase despite efforts of government and other concerned institutions to combat it. The university system in Nigeria is not exempt from this cankerworm. These citadels of learning, as it is, grapple with the challenge of indiscriminate substance use among undergraduates. It is against the backdrop of the prevalence of substance abuse among undergraduates and the consequent failures of so many strategies to combat abuse that this study investigated the acceptability of peer networking as an alternative public relations strategy for combating substance abuse among undergraduates of universities in Anambra State. The study was conducted in the light of the Theoretical Framework of Acceptability (TFA) propounded by Sekhon et al (2017). Using a descriptive survey design, the study surveyed a sample of 200 undergraduates from universities in Anambra State to ascertain their perspective on the acceptability of the proposed peer networking strategy in combating substance abuse. A questionnaire titled ‘Peer Networking for Combating Substance Abuse Questionnaire (PNCSAQ) was developed and used to obtain data from the undergraduates. The PNCSAQ was validated and the reliability coefficient index of 0.86 was obtained. Data collected for the study were analysed with descriptive statistics. Findings indicated that undergraduates despite their diverse demographic characteristics accepted peer networking as a good strategy for combating substance abuse in the universities. Based on the findings, it was recommended among other things, that PR managers should employ the peer networking strategy in combating substance abuse among undergraduates in the universities.
The Study of TPACK Ability Training for Student Teacher Specializing in English— A Case Study of Huainan Normal University
Authors: Ling Xiao , Chen Chen
Published: March 2024

It is a necessary requirement to enhance the information literacy of university teachers and students in an all-round way to adapt to the development of information society. At the heart of the work is an understanding of the framework Matthew J. Koehler and Punya Mishra proposed, TPACK—Technological Pedagogical Content Knowledge. This study takes student teacher specializing in English of Huainan Normal University as an example and aims to understand the current situation of developing the TPACK ability of the student teachers specializing in English through a survey. To investigate the relevant factors affecting the development of English teacher trainees’ TPACK ability, and to explore new ideas for revitalizing the TPACK ability of English teacher trainees to contribute to higher levels of teacher education accreditation and to cultivate higher quality student teachers specializing in English education.
Bidirectional encoders to state-of-the-art: a review of BERT and its transformative impact on natural language processing
Authors: Gupta Rajesh
Published: March 2024

First developed in 2018 by Google researchers, Bidirectional Encoder Representations from Transformers (BERT) represents a breakthrough in natural language processing (NLP). BERT achieved state-of-the-art results across a range of NLP tasks while using a single transformer-based neural network architecture. This work reviews BERT's technical approach, performance when published, and significant research impact since release. We provide background on BERT's foundations like transformer encoders and transfer learning from universal language models. Core technical innovations include deeply bidirectional conditioning and a masked language modeling objective during BERT's unsupervised pretraining phase. For evaluation, BERT was fine-tuned and tested on eleven NLP tasks ranging from question answering to sentiment analysis via the GLUE benchmark, achieving new state-of-the-art results. Additionally, this work analyzes BERT's immense research influence as an accessible technique surpassing specialized models. BERT catalyzed adoption of pretraining and transfer learning for NLP. Quantitatively, over 10,000 papers have extended BERT and it is integrated widely across industry applications. Future directions based on BERT scale towards billions of parameters and multilingual representations. In summary, this work reviews the method, performance, impact and future outlook for BERT as a foundational NLP technique.
 
We provide background on BERT's foundations like transformer encoders and transfer learning from universal language models. Core technical innovations include deeply bidirectional conditioning and a masked language modeling objective during BERT's unsupervised pretraining phase. For evaluation, BERT was fine-tuned and tested on eleven NLP tasks ranging from question answering to sentiment analysis via the GLUE benchmark, achieving new state-of-the-art results.
 
Additionally, this work analyzes BERT's immense research influence as an accessible technique surpassing specialized models. BERT catalyzed adoption of pretraining and transfer learning for NLP. Quantitatively, over 10,000 papers have extended BERT and it is integrated widely across industry applications. Future directions based on BERT scale towards billions of parameters and multilingual representations. In summary, this work reviews the method, performance, impact and future outlook for BERT as a foundational NLP technique.
Paratrochamminoides waskowskae n. sp., a new deep-water agglutinated foraminifera from the Paleocene of the Tasman Sea
Authors: Kaminski Michael A. , Korin Asmaa , Hikmahtiar Syouma
Published: March 2024

A new species belonging to the genus Paratrochamminoides is here described from the Paleocene at IODPSite U1511 in the Tasman Sea. The species Paratrochamminoides waskowskae n.sp. is characterized by its streptospiral coiling and elongated pseudochambers. A key to the identification of the Paratrochamminoides species is provided. Species are identified on the basis of their chamber shape and predominant mode of coiling.
Citizen instructors: Academic citizenship, graduate student instructors, and COVID-19
Authors: Dhingra Neil , Anthony Monica , Byrne Virginia , Hogan Erin
Published: March 2024

Academic citizenship entails seeing the academic community as worthy of love beyond what might prove useful for an annual performance review, even amidst a pandemic. The academic community must deserve what Harry Broudy called ‘enlightened cherishing’. Through a qualitative longitudinal study of graduate student instructors (GSIs) at a U.S. public university during the recent pandemic (2020-2022), involving multiple interviews and focus groups, we discovered that the GSIs remained academic citizens during emergency remote teaching necessitated by COVID-19 by adopting the notion of hustle. They were motivated to act with intensified resourcefulness and creativity because of a future-orientation in which a better tomorrow for the community was envisioned. They found more resources than those provided by their university, embraced flexibility, and recognized their students not just as students but as fellow human beings. The academic community, as opposed to the official institution, remained worthy of ‘enlightened cherishing’ because individuals within it could continually show flexibility and empathy in difficult times. To support this hustle, without it leading to burnout, we recommend that academic institutions foster, as an important part of academic citizenship, the virtue of misericordia, which Alasdair MacIntyre calls ‘grief or sorrow over someone else’s distress’—here for students caught up in tragedy.
A Comparative Study of Homoeopathic Decimal Versus Centesimal Potency Medicine in the Treatment of Cervical Spondylosis
Authors: Singh Swati , Gupta Neeraj
Published: March 2024

Background: Cervical spondylosis (CS) is a  degenerative condition of the cervical spine found  almost universally in persons over50 years of age. It  has become one of the common problems globally.Methods: An experimental study was conducted on  60 patients diagnosed with cervical spondylosis as per  the inclusion and exclusion criteria. All 60 cases of  cervical spondylosis were randomized into 2 groups  of 30 patients each. One group was prescribed with  Individualized Homoeopathic medicine in decimal  scale potency and the other group was prescribed  with centesimal scale potencies of Individualized  Homoeopathic medicine. The Remedy Response  Evaluation Form was used to assess and interpret  the follow up of the patients. The improvement of  the patients was assessed by the Neck Disability  Index (NDI). Result: Intra-group changes in NDI  score were found statistically significant (P < 0.001,  unpaired t test) group difference though favored  centesimal against decimal. Conclusion: On statistical analysis, the p value was  less than 0.001. Hence there is a significant difference  between post treatment score of centesimal potency  group as compared to decimal potency group.
Management of a Case of Chronic Suppurative Otitis Media with Homoeopathic Medicine Mercurius Solubilis - A Case Report
Authors: Sharma Navita , Kashyap Yogyata
Published: March 2024

Introduction: Chronic suppurative otitis media  (CSOM) is a long-standing infection of a part or  whole of the middle ear cleft characterized by  ear discharge and a permanent perforation. In  homoeopathy, there are various medicines having  a sphere of action on suppurative diseases. This is  a show case of Mercurius solubilise Homoeopathic  remedy for the case of CSOM: Mercurius solubilise is a black oxide of mercury. It affects the whole  system, penetrating every organ and tissue and also  has a wonderful effect on suppuration. Mercury, in  both acute and subacute forms, is the only drug that  effectively prevents suppuration.  Case summary: This is the case of 24 yr old man who  presented with CSOM and was successfully managed  with individualized Homoeopathic medicine, Mercurius Solubilis 200c. The case was followed  up with documentation of clinical symptoms and  investigation findings. In this article, the ultimate aim  is to provide the clinician with a homoeopathic tool in  the management of CSOM after having satisfactory  results in the case.
Enhancing Banking Chatbot Experience through Hybrid Conversational-Generative AI Approaches
Authors: Anish Shrimali
Published: March 2024

In today's digital age, chatbots have become an integral part of the banking industry, offering customers quick and efficient assistance. However, to meet the growing demands of customers and provide a seamless user experience, banking chatbots are evolving beyond rule-based systems. Traditional rule-based chatbots often fall short in handling complex queries and delivering personalized responses. This research proposal outlines a study aimed at enhancing the banking chatbot experience through the hybrid conversational-generative AI approaches.
The primary focus of this research is to combine the strengths of conversational AI, which excels in understanding and generating natural language responses, with generative AI, capable of generating creative and context-aware responses. By merging these two AI paradigms, banking chatbots can provide more human-like interactions, understand complex queries, and offer personalized responses
Image Super-Resolution using Convolutional Neural Networks
Authors: Rupesh Devidas Sushir
Published: March 2024

Image super-resolution is the process of enhancing the resolution of an image, typically from a lower resolution input to a higher resolution output. This research aims to explore the application of convolutional neural networks (CNNs) for image super-resolution. Specifically, the study will focus on developing a deep learning model capable of generating high-resolution images from low-resolution inputs. Various CNN architectures, such as SRCNN (Super-Resolution Convolutional Neural Network) or SRGAN (Super-Resolution Generative Adversarial Network), will be investigated and compared for their effectiveness in producing visually pleasing and perceptually accurate high-resolution images. Additionally, techniques such as residual learning, attention mechanisms, and adversarial training may be incorporated to further improve the quality of super-resolved images. The performance of the proposed models will be evaluated using standard image quality metrics and subjective assessments. This research has practical applications in enhancing the visual quality of low-resolution images in fields such as medical imaging, surveillance, and entertainment
Brain Tumor Detection: Transfer Learning Approach with VGG-16
Authors: Dr. K. Kalaivani  , V. Sravanthi  , T. Ruhina Begam  , H. Sai Anjana
Published: March 2024

Brain tumors have a profound impact on individuals worldwide, necessitating early detection to improve patient outcomes. This study explores the potential of deep learning in facilitating early tumor detection to aid medical professionals in timely intervention and personalized treatment strategies. While current tumor detection methodologies rely on traditional imaging techniques and manual analysis, they may suffer from limitations in accuracy and efficiency. To address these challenges, we propose a novel method using the VGG-16 architecture, a powerful Convolutional Neural Network (CNN) model, with pre-trained weights for feature extraction. By harnessing the capability of VGG-16, our model effectively discerns tumor patterns from brain MRI scans. The dataset utilized for training and evaluation consists of Brain MRI Images for Brain Tumor Detection from Kaggle, featuring a diverse range of annotated scans. During the evaluation, we focus on the primary performance metric of accuracy, which measures the model's ability to precisely classify tumor and non-tumor cases. Through extensive experimentation and analysis, we critically assess the model's performance and its potential clinical applicability
Entrevista com Ofélia Garcia
Authors: García Ofelia , Severo Cristine Gorski
Published: March 2024

Entrevista - Ofelia Garcia
A Synergistic Approach to Wildfire Prevention and Management using AI, Machine Learning, and 5G Technology in the United States
Authors: C. Stanley Okoro , Alexander Lopez , O. Austine Unuriode
Published: March 2024

In recent years, wildfires have emerged as a global environmental crisis, causing significant damage to ecosystems, and contributing to climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite advancements in detection methods, the increasing frequency of wildfires necessitates innovative solutions for early detection and efficient management. This study explores proactive approaches to detect and manage wildfires in the United States by leveraging Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology; Active monitoring and mapping with remote sensing and signaling leveraging on 5G technology; and Advanced response mechanisms to wildfire using drones and IOT devices. This study was based on secondary data collected from government databases and analyzed using descriptive statistics. In addition, past publications were reviewed through content analysis, and narrative synthesis was used to present the observations from various studies. The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively. This would save a lot of lives and prevent huge economic loss that is attributed to wildfire outbreaks and spread. Advanced technology can be used in several ways to help in the proactive detection and management of wildfires. This includes the development of the use of AI-enabled remote sensing and signaling devices and leveraging 5G technology for active monitoring and mapping of wildfires. In addition, super intelligent drones and IOT devices can be used for safer responses to wildfires. This forms the core of the recommendation to the fire Management Agencies and the government.
The Impact of AI on US Labor Markets
Authors: O. Austine Unuriode , C. Stanley Okoro , T. Osariemen Afolabi , M.Olalekan Durojaiye , Alexander Lopez , Y. Babatunde Yusuf , J. Mayowa Akinwande
Published: March 2024

This research explores the consequences of AI integration in the labor market. As AI shapes various industries, it brings a dual impact: displacing some jobs while creating others. The automation driven by AI could be a threat to routine tasks, potentially leading to the displacement of specific roles within the routine tasks. However, AI also creates new job opportunities, particularly in AI development and related fields. This study aims to analyze the multifaceted impact of AI on US jobs, considering displacement, creation, and skills. The research considered the following aspects: Evaluation of job displacement and creation, skill shifts, the quality of AI’s impact on performance, and exploring the relationship between AI models and human tasks. We were able to show AI's influence on the tasks performed by humans. The negative relationship between AI influence and tasks performed by humans shows that AI indeed has a notable and statistically significant adverse impact on human-performed tasks. We discovered that as AI technology advances and becomes more prevalent, certain tasks and roles traditionally carried out by humans are being automated or replaced by machines. Also, we were able to show the relationship between the AI model and human-performed tasks. It was found that AI models exhibit a substantial and statistically significant positive relationship with tasks performed by humans. Our findings suggest a more optimistic outlook for the labor market, where rather than displacing jobs and workers, AI technologies have the potential to enhance their capabilities and create new opportunities.
STRCacheML: A Machine Learning-Assisted Content Caching Policy for Streaming Services
Authors: Mahara Arpan , Fuentes Jose , Poellabauer Christian , D. Rishe Naphtali
Published: March 2024

Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions. Despite many studies conducted toimprove cache replacement strategies, there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have tested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, presenting comparative results compared to the established approaches based on raw cache hits and cache hit rates.
AI-Driven Simplification of 3D Animation: Bridgingthe Gap between 2D and 3D with a Unity Package for predictive Pose Generation and Streamlined workflows
Authors: Li Jiaxu , Morris John
Published: March 2024

This paper addresses the challenge of simplifying 3D animation by introducing a Unity package that harnesses artificial intelligence (AI) to convert 2D images or videos into 3D animation frames [2]. The background to this problem lies in the arduous and time-consuming nature of 3D animation, which often deters developers and artists from pursuing their creative visions [1]. Our proposed solution leverages AI algorithms to predict 3D poses and movements from 2D sources, making animation more accessible and cost-effective. Our package utilizes vector mathematics and Unity's capabilities, primarily focusing on establishing the body as an anchor for limb rotations. Challenges included intricate angle calculations and addressing orientation discrepancies. We resolved these challenges by refining the AI algorithms and providing user-friendly features [4]. Experimentation involved assessing accuracy, usability, and efficiency. While accuracy in complex scenarios remains a challenge, user feedback highlighted its potential for efficiency and time-saving. Ultimately, this tool bridges the gap between 2D and 3D animation, offering accessibility, costeffectiveness, and streamlined workflows [3]. Its potential impact on animation and game development makes it a valuable addition for both professionals and enthusiasts.
A Perceptive Mobile Program to Analyze Finger Posture on the Piano using Machine Learning and Object Detection
Authors: Tsukiyama Takuto , Ooi Sho , Sano Mutsuo
Published: March 2024

People from various professions are involved in the production of anime, including directors, animation directors, character designers, and voice actors/actresses. Specifically, the role of an animation director is of importance in the realm of animation. The animation director serves as the unifying force in shaping the animation's style by meticulously reviewing and redrawing the key animations provided by the key animators. The aim of this study is to develop a redrawing system using GLCIC to reduce the workload on animation directors when redrawing original key animation. Specifically, this study devised a system that employs GLCIC to analyze and learn the distinctive drawing styles of individual animation directors from images of their work and subsequently apply those styles to the conversion process. In the experiment, we asked people whose hobby is drawing to experience the developed system, and conducted a qualitative evaluation using a questionnaire and a quantitative evaluation using KLM analysis. As a result, we found that there were issues with ease of modification and UI. Additionally, the KLM analysis revealed that improving the system could reduce work time by a quarter. In the future, we think to improve the system with the aim of increasing work efficiency.
Machine Learning Training Optimization using the Barycentric Correction Procedure
Authors: Ramos-Pulido Sofía , Hernández-Gress Neil , G. CeballosCancino Héctor
Published: March 2024

Machine learning (ML) algorithms are predictively competitive algorithms with many humanimpact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy.
Study of Voice Generation Method Suitable for Characters based on Human Cognitive Characteristics
Authors: Saito Shogo , Ooi Sho , Sano Mutsuo
Published: March 2024

Previous studies have attempted to estimate existing voices from images of animated characters as a way to generate voices suitable for animated characters, but without good results. Therefore, in this study, to link the voice characteristics to match the animation character with the image, we devised a method to analyze the voice’s tendency to not be uncomfortable and then establish the ratio of voice learning data based on the analyzed tendency data. Specifically, this study prepares multiple voices for one illustration of an anime character, asks subjects to evalu- ate the voices, and calculates an evaluation based on the evaluation values. In experiments, we conducted an evaluation experiment using the one-pair comparison method, calculated the distri- bution of learning data based on the evaluation values obtained, and prepared for the subsequent learning process.
SAS-BERT: BERT for Sales and Support Conversation Classification using a Novel Multi-Objective Pre-Training Framework
Authors: Varma Aanchal , Bhat Chetan
Published: March 2024

Recent emergence of large language models (LLMs), particularly GPT variants has created a lot of buzz due to their state-of-the-art performance results. However, for highly domainspecific datasets such as sales and support conversations, most LLMs do not exhibit high performance out-of-the-box. Thus, fine-tuning is neededwhich many budget-constrained businesses cannot afford. Also, these models have very slow inference times making them unsuitable for many real-time applications. Lack of interpretability and access to probabilistic inferences is another problem. For such reasons, BERT-based models are preferred. In this paper, we present SAS-BERT, a BERT-based architecture for sales and support conversations. Through novel pre-training enhancements and GPT-3.5 led data augmentation, we demonstrate improvement in BERT performance for highly domainspecific datasets which iscomparable withfine-tuned LLMs. Our architecture has 98.5% fewer parameters compared to the largest LLM considered, trains under 72 hours, and can be hosted on a single large CPU for inference.
An Efficient Domain-Independent Approach for Supervised Keyphrase Extraction and Ranking
Authors: Ramaswamy Sriraghavendra
Published: March 2024

We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly lightweight ensemble model. Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several state-of-the-art baseline models, including all deep learning-based unsupervised models compared with, and is competitive with some supervised deep learning-based models too. Despite the supervised nature of our solution, the fact that does not rely on any corpus of “golden” keywords or any external knowledge corpus means that our solution bears the advantages of unsupervised solutions to a fair extent.
WorkTune: A Music-Assisted Writing Efficiency Evaluation and Promotion Platform using Artificial Intelligence and Natural Language Processing
Authors: Zhao Qizhen , Onuegbu Tochi
Published: March 2024

In a world where music accompanies various tasks, our paper addresses the challenge of understanding the impact of background music on work efficiency. The background problem centers on the lack of precision in existing studies, overlooking individual preferences and work types. Our proposed solution is a Python-based application that evaluates an individual's work efficiency while listening to different music genres [1]. The user-friendly interface incorporates features like music category selection, login options, and real-time statistics tracking [2][3]. Challenges, such as diverse user interactions and limited data, were addressed through a feedback channel for continuous improvement. The application underwent experiments, including regression model evaluations for essay grading and SVM parameter tuning [4]. Results indicated superior performance, emphasizing the relevance of ensemble learning and optimal parameter selection. This application provides a nuanced understanding of how background music influences work efficiency, offering a personalized approach that people can leverage for enhanced productivity and satisfaction in various work scenarios.
Reflection of Federal Data Protection Standards on Cloud Governance
Authors: Dye Olga , Heo Justin , Cankaya Ebru Celikel
Published: March 2024

As demand for more storage and processing power increases rapidly, cloud services in general are becoming more ubiquitous and popular. This, in turn, is increasing the need for developing highly sophisticated mechanisms and governance to reduce data breach risks in cloud-based infrastructures. Our research focuses on cloud governance by harmoniously combining multiple data security measures with legislative authority. We present legal aspects aimed at the prevention of data breaches, as well as the technical requirements regarding the implementation of data protection mechanisms. Specifically, we discuss primary authority and technical frameworks addressing least privilege in correlation with its application in Amazon Web Services (AWS), one of the major Cloud Service Providers (CSPs) on the market at present.
Computecycle: An IoT-Enabled Framework for Combating Obesity via Energy- Harvesting Fitness Equipment for Volunteer Computing Networks
Authors: Ku Jerry , Sun Sikang , Sun Yu
Published: March 2024

This paper presents ComputeCycle, an innovative solution addressing the pressing issues of obesity and energy consumption through a unique fusion of physical exercise, energy generation, and volunteer computing. The background to this problem lies in the global obesity epidemic and the need for sustainable energy sources. ComputeCycle tackles these challenges by motivating individuals to engage in physical exercise, which simultaneously generates electrical energy to contribute to scientific research through volunteer computing. The proposal outlines the key components of ComputeCycle, including a customized bike system with a DC motor, 3D-printed wheels for efficient energy transfer, and a diode-based energy flow control mechanism. Challenges related to wheel attachment and energy flow direction were resolved through innovative 3D printing and diode integration. Experiments showcased ComputeCycle's effectiveness in promoting exercise, energy generation, and volunteer computing, resulting in positive outcomes for health and scientific progress. Our findings emphasize the feasibility of this holistic approach to combat obesity and contribute to sustainable energy solutions. Ultimately, ComputeCycle offers a compelling solution that motivates individuals to improve their health while actively participating in scientific research, making it a valuable and impactful tool for individuals and communities seeking to address both health and environmental challenges.
A Machine Arm to Assist in Trash Sorting using machine Learning and Object Detection
Authors: Fu Shihan , Li Ang
Published: March 2024

Addressing the global challenge of inefficient waste management, my paper introduces an innovative recycling solution integrating machine learning, computer vision, and a robotic arm [1]. The background problem revolves around inaccurate waste sorting and the environmental impact of recyclables ending up in landfills. The proposed solution involves a sophisticated machine learning model for object recognition, a computer vision system for real-time detection, and a robotic arm for precise object manipulation [2]. Challenges included optimizing the machine learning model for diverse materials and enhancing the robotic arm's adaptability. Experimentation involved testing the system's efficiency in various scenarios, showcasing its ability to recognize and sort recyclables accurately. The results demonstrated promising accuracy and adaptability. Ultimately, this solution offers a practical and automated approach to waste sorting, reducing environmental impact, and promoting efficient recycling practices, making it a valuable tool for waste management systems globally [3].