bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning Lessons from COVID -19 Pandemic Crisis: An Opportunity for Strategic Leadership to Transform Economies in the Sub-Saharan Africa through Innovation.
Authors: Musara Godfrey , Nyakora Prof. Musa
Published: August 2023

COVID -19 has bought untold suffering to various communities around the globe without any solution in the near future. It is these moments of crisis that great champions and economies emerge from. Strategic leadership should focus on how to turn moments of economic depression to come up with economic solution. It is through innovation from simple to complex, from individuals to organizations, and global culture of innovation that Strategic leadership will develop solution to change the world. This paper focuses on simple innovative solution to address some of the complex challenges economies are going in the Sub- Saharan Africa specifically in Southern Africa Development Community (SADC). This paper challenges that the absence of a solution to the COVID- 19 is a sign that the world has done little in terms of dealing with complex challenges, innovation and change. The paper suggests that no problem should be dealt with in a similar way but through creative thinking and innovation leadership should look at every challenge differently from the other. The paper gives a historical details on the discovery of the COVID -19 in China and spreading to almost every country of the world. It then focuses on a handful countries in the region and how leadership responded the pandemic and how it has affected the continent’s economy . It then suggests how the challenges can be dealt with.
Child Response Styles to Parenting and Mental Health among Adolescents in Embakasi East sub county, Nairobi County, Kenya
Authors: Ayako Lornah Irene
Published: August 2023

Children’s reaction to their parents is likely to have significant impacts on their mental health. Positive reactions to parents are prone to promote feelings of safety, security and trust which are essential for healthy development of a child. On the other hand, negative reactions to parents may tend to stimulate feelings of fear, uncertainty and anxiety which can negatively impact a child’s mental health. Many studies have been carried out on how parents take care of their children. However, not much has been explored on the manner in which children react on the way they are being brought up. It is thus important to explore how children react towards their parents and the effects it has on their mental health. The study was anchored on Bowlby’s attachment theory and employed a quantitative approach, descriptive research design. The sample population was 250 consisting of male, female and others aged 15 – 18 living in Embakasi East sub county, Nairobi County, Kenya. Convenience sampling technique was used to get the final sample from potential participants who were willing and met the requirements needed for participation in the survey. Data was collected using sociodemographic questionnaire, child response style by Egunjobi (2021)to evaluate child response styles to parenting and Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) to determine their mental well-being. Data was coded in SPSS and analyzed using descriptive statistics. From the results, it was alluded that adherer was the most child response style (M = 3.56) followed by nonchalant response style (M = 2.53).The falser response style was at (M=2.13) and lastly the rejector response style was low at (M=2.06).The findings revealed that adolescents who are adherers generally have a high mental health (M=3.14)compared to those who have rejector, falser and non-chalant child response styles. The study also showed that there was a weak positive relationship between adherer child response style to parenting and mental wellbeing,[r = .273, n = 250].Additionally, there was also a weak negative relationship between falser child response style to parenting and mental wellbeing,[r = -.326, n = 250].The study therefore concludes that adherer child response style to parenting positively impacts the mental health of an adolescent compared with the rejector, falser and non – chalant child response style. The study recommends incorporating healthy practices in parenting styles that encourages children to be adherers hence minimizing mental health issues among adolescents. The study findings are useful to mental health experts, teachers and parents to understanding mental health of adolescents as a result of child response styles. The findings also draw attention to the gaps in knowledge on child response styles and mental health of adolescents. The study recommends further research child response styles to parenting and other variables.
ESTÁGIO DE INTEGRAÇÃO DA TECNOLOGIA NO ENSINO MÉDIO: POTENCIALIZANDO O PROCESSO DE ENSINO E APRENDIZAGEM
Authors: Ramos Daniel do Nascimento , Barbosa Luiz Sérgio de Oliveira , Oliveira Rogério Brito de , Pinho Arley Silva de , Batista Francisco Rayner Barros , Silva Gabriel Natividade da
Published: August 2023

Este artigo apresenta as experiências vivenciadas no Estágio Supervisionado em Computação, no Curso de Licenciatura em Computação, da Universidade do Estado do Amazonas (UEA), no Núcleo de Ensino Superior de Coari (NESCOA), no qual houve a  integração da tecnologia no ensino médio, e foram exploradas estratégias para potencializar o processo de ensino e aprendizagem. O objetivo principal foi investigar como as tecnologias podem ser incorporadas de forma efetiva e significativa no contexto educacional, visando o desenvolvimento de competências dos alunos. Durante o estágio, foram observadas as aulas e realizadas atividades práticas, utilizando recursos tecnológicos como ferramentas de apoio ao ensino. O planejamento pedagógico foi direcionado à seleção adequada das tecnologias, à definição de objetivos de aprendizagem claros e à adaptação das estratégias de ensino. Além disso, foi realizado um acompanhamento do impacto das atividades no processo de ensino e aprendizagem dos alunos. Os resultados obtidos evidenciaram que a integração da tecnologia proporcionou maior engajamento dos alunos, estimulando a criatividade, o pensamento crítico e a colaboração. Conclui-se que a utilização da tecnologia no ensino médio pode ser um recurso valioso para enriquecer as práticas educacionais, promovendo uma educação mais atualizada e alinhada às demandas da sociedade digital.
UM OLHAR SOBRE O DESENVOLVIMENTO DO PENSAMENTO CRÍTICO E DA CRIATIVIDADE DOS ALUNOS DA EEEP MARIA JOSÉ MEDEIROS A PARTIR DO PROCESSO DE APRENDIZAGEM DURANTE AS AULAS DE GEOGRAFIA
Authors: Alves da Silva Everton
Published: August 2023

Esse estudo objetivou analisar a criatividade e o pensamento crítico dos estudantes do ensino médio, a partir de observações e questionamentos que estimulem a participação ativa na disciplina de Geografia, aplicados na Escola Estadual de Educação Profissional Maria José Medeiros da rede pública do estado do Ceará. Esse estudo ainda objetivou identificar os recursos utilizados pelos estudantes para compreenderem a realidade, identificando os principais meios utilizados por eles para compreenderem a própria realidade e as possíveis causas das dificuldades de aprendizagem. Para tanto, foi utilizado como método para coleta de dados a pesquisa bibliográfica. A partir da análise de dados foi possível perceber a importância de dar acesso aos estudantes a diversos recursos pedagógicos e diferentes metodologias de ensino, dentre elas, a organização de seminários, o estímulo à pesquisa em diferentes meios, desde que sejam confiáveis. Promover a autonomia e a capacidade de criar algo novo, buscar soluções para os desafios cotidianos, desenvolver a criticidade, ser protagonista, debater com consciência e sugerir melhores formas de aprendizagem. Enfim, por meio de todo o estudo realizado e das sugestões pedagógicas apresentadas foi possível confirmar que o desenvolvimento da criatividade e do pensamento crítico são competências necessárias ao enfrentamento de todos os desafios do século XXI, em todos os âmbitos da vida humana. Essas competências devem fomentar o processo educacional para obter uma educação mais interativa, dinâmica e significativa.
Individualism and Nonattainment of Social Change in Chinua Achebe’s Things Fall Apart and Arrow of God
Authors: Ohia Ben-Fred , Oroworukwo Nkpolu
Published: August 2023

Purpose: In a society where many believe that only the mailed fist can maintain discipline, a refreshingly different point of view is reached by the comparison of two of Chinua Achebe’s novels: Things Fall Apart and Arrow of God. The purpose of this paper is to explore the viewpoint presented in these novels by comparing their two heroes – Okonkwo and Ezeulu. They are compared first as revolutionary characters and second as revolutionary leaders, using as a guide Calvert’s (1996) claims that individual leaders are not necessarily revolutionary leaders. Okonkwo and Ezeulu are seen to be both leaders and revolutionaries; they fail however, as revolutionary leaders for the simple but unarguable reason that they have no supporters.

Methodology: Methodological procedures are adopted which include description of the study’s sampling and method of data analysis. The study adopts a simple survey design.

Findings: The paper in its findings discovers that individualism cannot thrive in a revolution of a society rather it is collective effort that appears to be more effective in a revolutionary struggle such as fou nd in Umuofia and Umuaro respectively. The paper concludes that followership marks an authentic revolutionary leader as the non-support of Okonkwo and Ezeulu in the novels leads to their tragic end.

Unique contribution to theory, policy and practice: The paper recommends communalism as a way out of African socio-political barriers and posits that individuality cannot bring the social change and total liberation in African continent.
FakeExpose: Uncovering the falsity of news by targeting the multimodality via transfer learning
Authors: Kalra Sakshi , Kumar Chitneedi Hemanth Sai , Sharma Yashvardhan , Chauhan Gajendra Singh
Published: August 2023

Social media for news utilization has its own pros and cons. There are several reasons why people look for and read news through internet media. On the one hand, it is easier to access, and on the other, social media’s dynamic content and misinformation pose serious problems for both government and public institutions. Several studies have been conducted in the past to classify online reviews and their textual content. The current paper suggests a multimodal strategy for the (FND) task that covers both text and image. The suggested model (FakeExpose) is created to automatically learn a variety of discriminative features, instead of relying on manually created features. Several pre-trained words and image embedding models, such as DistilRoBERTa and Vision Transformers (ViTs) are used and fine-tined for the best feature extraction and the various word dependencies. Data augmentation is used to address the issue of pre-trained textual feature extractors not processing a maximum of 512 tokens at a time. The accuracy of the presented model on PolitiFact and GossipCop is 91.35 percent and 98.59 percent, respectively, based on current standards. According to our knowledge, this is the first attempt to use the FakeNewsNet repository to reach the maximum multimodal accuracy. The results show that combining text and image data improves accuracy when compared to utilizing only text or images (Unimodal). Moreover, the outcomes imply that adding more data has improved the model’s accuracy rather than degraded it.
Deep learning based phishing website identification system using CNN-LSTM classifier
Authors: Sapkal Vinod , Gupta Praveen , Khan Aboo Bakar
Published: August 2023

The term phishing refers to an attack that pretends to be the website of a large corporation, typically one dealing with money, such as a bank or other financial institution or an online retailer. Its primary objective is to acquire personally identifiable information from users, such as their social security numbers, credit card information, and passwords. Due to the rise of phishing attacks, various techniques have been developed in order to combat these threats. One of these is deep learning algorithms, which are capable of learning and analyzing massive datasets. Due to their capabilities, these algorithms are very useful in identifying and preventing phishing attacks. Due to the complexity of the phishing websites, many development systems have been created to detect them. Unfortunately, the output that was desired cannot be achieved by these systems, and they have a number of other flaws as well. The purpose of this paper is to propose a hybrid deep learning-based phishing detection system that is easy to put into practice. The quality of the input dataset is improved through the process of preprocessing the dataset. After that, the procedures of clustering and feature selection are carried out in order to improve the accuracy and decrease the amount of time required for the processing. The resulting features are then fed into the CNN_LSTM, which is a classification system that classifies websites that are phishing and legitimate. Proposed Hybrid deep learning models are proposed to combine the features of natural language processing (NLP) and character embedding. They can then reveal high-level connections between characters. In terms of the metric that is being used for the evaluation, the performance of the models that have been proposed is better than that of the other models.
Optimized data analysis through hybrid approach over trusted security environment
Authors: Sharma Satyajeet , Sharma Bhavna
Published: August 2023

For those who can uncover the knowledge hidden inside this data, this offers enormous opportunity, but it also creates new difficulties. In this study, we explore how the contemporary discipline of data mining might be used to glean usable information from the data that surrounds us. k Machine learning techniques include genetic algorithms, Bayesian approaches, and nearest neighbor. By combining these approaches and algorithms, a hybrid method is created in this study. The goal is to successfully categorize data by removing any information that makes it harder to learn. According to solid facts at hand, a novel data set formation strategy is suggested. Five datasets for machine learning from UCI are used in the testing procedure. These data sets pertain to the iris, breast cancer, glass, yeast, and wine. The success of the research is taken into consideration when test findings are analyzed in conjunction with prior efforts.
Artificial intelligence-based classification performance evaluation in monophonic and polyphonic indian classical instruments recognition with hybrid domain features amalgamation
Authors: Chitre Abhijit V. , Wanjale Kirti , Deshmukh Aradhanaa , Kosbatwar Shyamsunder P. , Ingle Anup , Hundekari Sheela
Published: August 2023

In computer music, instrument recognition is a critical part of sound modeling. Pitch, timbre, loudness, duration, and spatialization are all components of musical sounds. All of these components play a significant part in determining the quality of the tonal sound. It is possible to alter the first four parameters, but timbre always poses a challenge [6]. It was inevitable that timbre would take center stage. Musical instruments are distinguished from one other by their distinct sound quality, independent of their pitch or volume. To distinguish between monophonic and polyphonic music recordings, this method might be used. In Musical Information Retrieval, classification plays one of the critical role. Monophonic instrument classification can be found in literature with quiet a substantial combinations of features and classifiers. Polyphonic instrument classification witnessed less references in the literature and is still an area to be explored specifically when it comes to Indian Classical domain. The present paper exactly focusses on this experimentation.  Several Indian instruments were used to produce training data sets for the proposed approach’s evaluation purposes. Among the instruments utilized are the flute, harmonium, and sitar. Statistical and spectral factors are used to classify Indian musical instruments along with the Artificial Intelligence-based methods. Hybrid features from multiple domains that extract essential musical properties are extracted. Accuracy is demonstrated through an Indian Musical Instrument SVM and GMM classification. With monophonic sounds, SVM and Polyphonic produce an average accuracy of 89% and 91%. GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33, according to the results of the studies. The future scope of this recognition framework can be an Artificial Intelligence System with a system linked with the Industrial Internet of Things (IIOT) framework to develop a standalone system or application which can be used for real- time classification of instruments.
Development of object identification model with deep reinforcement learning algorithm
Authors: Naidu P. Ramesh , Sharma Avinash , Diwan Supriya P. , Gowda V. Dankan , Pandya Parth M. , Gupta Anand Kumar
Published: August 2023

This research work presents an object identification method based on the machine learning technique based on human vision system. The objective is to prevent processing a complete image in sort to locate objects. Presently, the-state-of-the-art techniques divide an image into sub-regions and search for an object in all the subparts. This is ineffective for applications like embedded systems where the computation power is restricted or the resolution of the images are high. To address this issue, an object identification task was formulated as a decision-making problem. Followed the concept of DRL proposed, accepted RL algorithm DQL was applied to learn a policy from input data, i.e. images, to identify objects in a scene. In this manner, with the policy learned, a set of actions that transforms a box was apply in order to make tighter a bounding box around the target object.
An ontological architecture for context data retrieval and ranking using SVM and DNN
Authors: Mudgil Pooja , Gupta Pooja , Mathur Iti , Joshi Nisheeth
Published: August 2023

Context retrieval and ranking have always been an area of interest for researchers around the world. The ranking provides significance to the data that has to be presented in front of users but it also consumes time if the ranking architecture is not organized. The retrieval is dependent upon the co-relation among the data attributes that are supplied against a class label also referred to as ground truth and the ranking depends upon the sensing polarity that indicates the hold of the outcome towards asked information. This paper illustrates an ontological architecture that involves two phases namely context retrieval and ranking. The ranking phase is composed of three different algorithm architectures namely k-means, Support Vector Machines (SVM), and Deep Neural Networks (DNN). The DNN is tuned to fit and work as per the availability of a total number of samples. The proposed work has been evaluated for both quantitative and qualitative parameters in different sets and scenarios. The proposed work has also been compared with other state of art techniques and is illustrated in the paper itself.
Breast cancer prediction using supervised machine learning techniques
Authors: Dadheech Pankaj , Kalmani Vijay , Dogiwal Sanwta Ram , Sharma Vijay Kumar , Kumar Ankit , Pandey Saroj Kumar
Published: August 2023

Breast cancer is one of the most prevalent diseases in India’s urban regions and the second most common in the country’s rural parts. In India, a woman is diagnosed with breast cancer growth every four minutes, and a woman dies from breast cancer sickness every thirteen minutes. Over half of breast cancer patients in India are diagnosed with stage 3 or 4 illness, which has extremely low survival rates; hence, an urgent need exists for a rapid detection strategy. To forecast if a patient is at risk for breast cancer, we utilise the classification techniques of machine learning, in which the machine learning model learns from the previous information and can anticipate on the new information that is generated by the data. To create a model using Logistic Regression, Support Vector Machines, and Random Forests, this dataset was collected from the UCI repository and studied in this study. The primary goal is to improve the accuracy, precision, and sensitivity of all the algorithms that are used to categorise data in terms of the competency and viability of each and every algorithm. Random Forest has been shown to be the most accurate in classifying breast cancer, with a precision of 98.60 percent in tests. The Scientific Python Development Environment is used to carry out this machine learning study, which is written in the python programming language.
Source-to-source translation for code-optimization
Authors: Chowdhary K. R. , Purohit Rajendra , Purohit Sunil Dutt
Published: August 2023

Multi-core design intends to serve a large market with user-oriented and highproductivity management as opposed to any other parallel system. Small numbers of processors, a frequent feature of current multi-core systems, are ideal for future generation of CPUs, where automated parallelization succeeds on shared space architectures. The multi-core compiler optimization platform CETUS (high-level to high-level compiler) offers initiates automatic parallelization in compiled programmes. This compiler’s infrastructure is built with C programmes in mind and is user-friendly and simple to use. It offers the significant parallelization passes and also the underlying empowering techniques, allows source-to-source conversions, and delivers these features. This compiler has undergone numerous benchmark investigations (techniques) and approach implementation iterations. It might enhance the programs’ parallel performance. The main drawback of advanced optimising compilers, however, is that they don’t provide runtime details like the program’s input data. The approaches presented in this paper facilitatedynamic optimization using CETUS. The large amount of proposed compiler analyses and modifications for parallelization is the last point. To research the behaviour as well as the throughput gains, we investigated both non-CETUS based and CETUS based parallelized program features in this work.
Forecasting stochastic consumer portability visitation pattern in fair price shops of India
Authors: Sasi Archana , Subramanian Thiruselvan
Published: August 2023

In India, the Public Distribution System (PDS) is a critical tool for accomplishing the aim of “Zero Hunger”. Despite the enormous resources used, PDS has several inefficiencies that are caused by the monopoly of agents engaged in last-mile grain supply. Various state governments in India have been employing portability as an innovative solution to address this problem. In this article, we examined a huge-scale data on the deployment of portable beneficiaries arriving in a particular FPS of Kerala state in India over three years. A comparison is made between Auto-Regressive Integrated Moving Average (ARIMA) method which makes forecasts in univariate data and ARIMA with exogenous variables called ARIMAX. We followed Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) as the accuracy performance measure of the models and observed that the ARIMAX model outperforms the ARIMA model with the least forecasting errors.
Enhancing the accuracy of breast cancer detection and determination of risk factor by using the backpropagation network theory and SVM: Machine learning
Authors: Madhavi N. Bindu , Dohare Sushil , Prasad G. N. R. , Babu D. Vijendra , Al-Ansari Abdul Rahman Mohammed
Published: August 2023

According to the world health organization, every year, more than 8% of women suffer due to breast cancer, and 40% of women die in low-poverty regions. This entire work focuses on the algorithm to detect breast cancer. This algorithm improves the accuracy of the detection and the risk factor determination by using the backpropagation network (BPN) theory and the Support vector method (SVM). By the end of the entire work, the improved accuracy is up to 95% compared to other forms; this proposed method is proper when evaluating the patient report in the image format, like a scanning report.
Mathematical data analytic model for investment patterns assessment in stocks of selected sectors
Authors: Gautam Arun , Goyal Ruchi
Published: August 2023

The investment patterns adopted by various Companies include either internal or external sources of funds, to invest in diverse options within the capital market and money market instruments. This paper is to propose a mathematical analytic model that explores corporate investment patterns and their subsequent impact on profitability, borrowings, and other parameters of corporate health. Specifically, the paper intends to examine the immediate effect of investments on the company’s Profit After Tax (PAT), excluding investments in Fixed Assets. Furthermore, the study seeks to provide a mathematical comparative analysis using collected data on Retained Earnings, a critical component of profitability, and investments. By suggesting a mathematical data analytic model, this research paper also addresses the reasons why many businesses continue to rely on outdated methods and conventional choices, apart from Fixed Assets.
Co-CEP: A co-designed community engagement protocol as a catalyst for cybersecurity research in Africa: The case of northern Namibia
Authors: Nhinda Gabriel Tuhafeni , Shava Fungai Bhunu
Published: August 2023

Through technology, the world has become a global village, connecting geographically distant people into virtual neighbors. This presents many opportunities for individuals, businesses, and remote communities. The advantages of this connectivity have been brought to the fore by COVID-19, as technology and the Internet kept the world connected and functioning. However, in Africa, although much has been done on connecting the unconnected, there remains rural underserved communities. Notwithstanding the benefits of technology and the Internet, the cyber-safety and cybersecurity of those communities living on the fringes of society – rural underserved communities – is unknown. Consequently, many researchers, noting the benefits of technology, have embarked on ICT for Development projects (ICT4D). However, community based ICT4D projects have been failing due to myriad of factors. Some such factors are engagement misalignment, cultural faux pas’, communication, power dynamics and community-buy-in of end-products. With the propensity to circumvent the shortcomings indicated above, Participatory Design (PD) processes may be utilized. Co-design extends PD by allowing researchers and participants to be equals in the creation of solutions to problems contrary to traditional research approaches, that abstract researchers from the community within which they work. As an effort to address these challenges, we present a co-designed community engagement protocol (co-CEP) for rural underserved communities in Northern (Oukwanyama), Namibia. This study was qualitative, guided by Ubuntu and Uushiindaism tenets of trust, neighbourliness, respect, familiarity, hospitality, and collective unity as pillars of fostering communication within 4 villages in Oukwanyama, Namibia. The aim was to understand the concepts of how information flows in order to get engagement from community members for cybersecurity research. We held 4 co-design sessions in 4 villages, with each feeding into the next. The presented co-CEP comprises of 13 elements that are key to successful community engagement within community-based technology projects, especially cybersecurity research. The co-CEP is part of a research study on co-designing cybersecurity practices with rural underserved communities.
FakeSpotter: A blockchain-based trustworthy idea for fake news detection in social media
Authors: Kalra Sakshi , Bansal Yash , Sharma Yashvardhan , Chauhan Gajendra Singh
Published: August 2023

Social media encourages information sharing without a physical barrier making it the perfect platform for learning and communication. In the meantime, it acts as a means of quickly disseminating misleading information. Researchers are battling fake news using strategies like detection, verification, mitigation, and analysis because of significant social concerns. It can be hard to tell the difference between true and false information. In the area of knowledge verification, various machine and deep learning-based approaches have been used to identify false data. However, there are some drawbacks of using AI-powered technologies, including data dependency, security concerns when applying AI-powered methods in the real world, and gaining user trust. In order to address the issues with AI-powered technologies, a blockchain-based idea (FakeSpotter) is put forth in this work. We offer an idea i.e.based on blockchain that utilizes crowdsourcing to determine whether or not content is fake. We attempt to use Blockchain technology’s features correctly and completely to create a secure system with no authoritative control over information dissemination. In this attempt, we aim to build a system that is not reliant on pre-defined datasets and discuss the initiatives taken in the fight against disinformation.
An approach for predicting the price of a stock using deep neural network
Authors: Pandey Dhiraj , Jain Megha , Pandey Kavita
Published: August 2023

For the prediction of any stock price and its fluctuations in prices, researchers have suggested several versions of machine learning techniques. Machine learning-based techniques fail to achieve good prediction and in turn, their accuracy is not adequate to predict the stock price. For sentiment analysis related to the financial domain BERT model is quite useful.  The score generated by BERT is useful to get more insight. Few research works which have incorporated financial news, have not used financial corpus for training and testing. FinBERT is quite useful to solve stock pricing fluctuations as it is specially trained on corpus related to the financial domain. The stock market usually gets fluctuated during any impactful news either positive or negative sentiments. In this work, highly fluctuating stock price movement is predicted efficiently which is validated by experiment analysis. Further, in existing research works, stock prices are predicted for a specific company only. In this paper, A hybrid method to predict fluctuations in stock prices has been suggested using FinBERT and Long Short-term Memory (LSTM) along with news that impacted the market. The proposed method using news score and hybrid approach outperforms existing approaches significantly.
Stochastic optimization of multi-capacitated vehicle routing problem with pickup and delivery using saving matrix algorithm
Authors: Bhuranda Lokesh Kumar , Rizwanullah Mohd. , Sharma Arpit Kumar , Gautam Kamlesh , Chawla Yash
Published: August 2023

The Multi-Capacitated Problem is an optimization problem. To reduce the overall distance, the optimization problem seeks out vehicle routes that connect every customer to a storage facility. This article uses a saving matrix approach to propose an extended Vehicle Routing Problem that considers a stochastic environment and multiple capacitors. Stochastic customers are an essential element of the problem. A computational analysis also supports the suggested approach to obtain the best route.
Voluntariado corporativo: um estudo bibliométrico com VOSviewer
Authors: Da Silva Lopes Junior Derson , Figueiredo Milani Filho Marco Antonio
Published: August 2023

O voluntariado corporativo tem ganhado destaque na literatura prática e acadêmica de administração. Esse artigo elaborou uma análise bibliométrica relacionada ao voluntariado corporativo na base de dados Web of Science. Foram analisados todos os artigos da temática disponíveis na base, abrangendo o período de 1900 a 2019, utilizado o software VOSviewer para determinar as áreas de destaque pesquisadas, os idiomas de tais publicações, a tendência anual no número de publicações, os periódicos mais relevantes para publicação e os mais citados autores. O estudo revelou a dominância da língua inglesa entre os artigos da temática (97,08%), sendo que 29,7% foram produzidos nos Estados Unidos. Dentre as maiores citações presentes nos artigos objeto da amostra, estão o Nonprofit and Voluntary Sector Quarterly como periódico mais citado, John Wilson como autor mais citado, sendo seu artigo “Volunteering” o mais citado no tema. No que tange ao âmbito corporativo, percebe-se espaço para expansão de estudos no tema do voluntariado em suas diferentes expressões para as organizações. A pesquisa traz contribuição acadêmica ao oferecer um direcionamento para aqueles que desejam produzir pesquisas em performance organizacional, apontando para os pesquisadores e recursos mais proeminentes no tema. São apresentados elementos interessantes para aqueles que buscam pesquisar a temática, fornecendo informações sobre quais periódicos consultar e quais autores são mais eminentes.
Hospitality Trends in a Post-Pandemic World: Adaptation and Recovery
Authors: Claudine Sykimte
Published: August 2023

This paper undertakes a comprehensive exploration of the profound shifts and innovative strategies that are shaping the evolution of the hospitality sector in the aftermath of the crisis. Through a meticulous integration of quantitative and qualitative methodologies, this study delves into the intricate interplay of consumer preferences, technological advancements, sustainability imperatives, and operational paradigms that have come to define the industry's response to the new normal. The quantitative phase, encompassing a structured survey administered to a diverse sample of 1000 respondents, unveils a spectrum of evolving consumer behaviors, ranging from a heightened preference for outdoor and nature-centric activities to an increasing demand for eco-conscious accommodations and technology-driven contactless interactions. This is further complemented by the qualitative phase, involving in-depth interviews and focus group discussions with industry stakeholders, which provides nuanced narratives of innovation and resilience strategies adopted by businesses to adapt to the evolving landscape. Together, these insights reveal a mosaic of adaptation and recovery efforts, including the creative repurposing of spaces, local partnerships to foster authentic experiences, and the integration of technology for personalized services. As the hospitality industry charts its course forward, armed with the lessons from this research, it is poised to redefine its trajectory, embracing sustainability, technological integration, and guest-centric approaches as foundational pillars for a resilient and vibrant future. By synthesizing the quantitative and qualitative dimensions, this study offers a comprehensive understanding of the emerging trends and transformational trajectories that are shaping the post-pandemic hospitality realm, providing a roadmap for stakeholders to navigate the challenges and opportunities that lie ahead in this dynamic and reimagined era.
UtilityTrack Pro: Premier Hotel Room Utilities Management System
Authors: Claudine Sykimte
Published: August 2023

This study introduces UtilityTrack Pro an advanced system for managing hotel room utilities. By replacing manual methods with technology, the system enhances guest experiences, streamlines operations, and reduces losses. Rapid application development methodology was used in the system's design and development of the system. Results include improved guest satisfaction, optimized operations, and informed decision-making. UtilityTrack Pro exemplifies technological innovation in the hospitality sector, setting a new standard for guest services and operational efficiency.
Revolutionizing the Dining Experience: Exploring Digital Innovations in Food Ordering for Enhanced Customer Satisfaction
Authors: Claudine Sykimte
Published: August 2023

This research paper delves into the transformative potential of digital innovations in the realm of food ordering, focusing on their profound impact on customer satisfaction and the broader dining experience. By investigating the adoption of advanced technological platforms and their integration into traditional restaurant operations, this study uncovers the multifaceted benefits of digital food ordering systems. Through an analysis of existing literature, case studies, and a comprehensive exploration of implementation guidelines, this paper underscores how digitalization not only enhances customer convenience and accessibility but also offers opportunities for improved order accuracy, data-driven insights, and revenue generation. By navigating challenges such as technological adoption barriers and privacy concerns, this research contributes to a comprehensive understanding of the present and future landscapes of digital gastronomy.
Food and Beverage Management: Trends, Innovations, and Challenges
Authors: Claudine Sykimte
Published: August 2023

This paper is woven with dynamic trends, pioneering innovations, and formidable challenges, explored in the study of food and beverage management: trends, innovations, and challenges. Through an extensive literature review, the research traces F&B Management's evolution within the connection of culinary arts, hospitality, and business acumen, establishing a comprehensive foundation for understanding core concepts. Employing a mixed-methods approach, the study captures insights from professionals and discerning consumers, unveiling trends like sustainability and experiential dining, where 72% and 65% of respondents, respectively, emphasize preferences for locally sourced ingredients and immersive dining. Technological innovations play a pivotal role, with 42% of respondents favoring AI-driven personalized menu recommendations, and 58% desiring tech-enhanced customer convenience. Amid challenges such as labor shortages and health regulations, the industry exhibits adaptability and resilience, embracing mentorship and technology-driven solutions. It offers a panoramic view of an industry balancing tradition and transformation, guiding practitioners, researchers, and stakeholders through an ever-evolving landscape.
Evaluating Quality of Work Life of Employees of Selected Textile Units in Five Star MIDC, Kagal, Kolhapur
Authors: Dr. Gajanan J. Salunkhe
Published: August 2023

Quality of work life is an effective tool for retaining employee and making the work pleasure for people. In The approach of quality of work life organization consider human resource as most important asset than any other thing in organization. It will help to those industries which are facing problems of employee retention . This study will assist the Indian textile companies to understand Human Resource practices. Quality of Work life now a days is more important than any other thing in human resource management. Quality of work life considers economic, social and psychological needs of people which in turn satisfy their work life & social life
Advancements in Electric Vehicle Charging Infrastructure: Fast Charging, Wireless Charging, and Smart Grid Integration
Authors: Jordan Y. Arpilleda
Published: August 2023

This study investigates the dynamic landscape of electric vehicle (EV) charging infrastructure, focusing on recent advancements in fast charging, wireless charging, and smart grid integration. The exponential growth of EV adoption has emphasized the urgency of efficient charging solutions. The historical context underlines the evolution of charging methods from conventional to advanced technologies. Fast charging's notable progress, with outputs exceeding 150 kW, has significantly reduced charging times, but the potential impact on battery health warrants consideration. Wireless charging introduces a paradigm shift by enhancing energy transfer efficiency and finding application in contexts where conventional connectors are impractical. Integrating EVs with the smart grid holds promise for demand response, load balancing, and renewable energy utilization. Challenges include safety concerns, standardization, and grid integration complexities. Comparing advancements with conventional methods reveals enhanced efficiency and convenience. This study contributes insights into the transformative potential of these technologies, paving the way for a sustainable and accessible future of transportation
Developing Servant Leadership Skills in Higher Education Leaders: A Literature Review
Authors: Dawit Negussie  , Dr. Jabe Bekele Hirgo
Published: August 2023

This systematic literature review aims to explore and synthesize existing research on the development of servant leadership skills among higher education leaders. As servant leadership gains recognition as a transformative leadership approach in educational settings, understanding effective strategies for cultivating servant leadership qualities becomes essential for fostering positive organizational culture and enhancing student outcomes. This article seeks to provide insights into various training programs, workshops, and initiatives that have been employed to promote servant leadership competencies, and their impact on leadership effectiveness, employee engagement, and overall institutional performance. The review also addresses the challenges and opportunities associated with implementing servant leadership development programs in the higher education context
Aural Auras of Inner Sounds
Authors: Sarbadhikary Sukanya
Published: August 2023

Situated at the interface of anthropological fieldwork and philosophical scrutiny, this chapter explores relations among sounds, bodies, and texts through the prism of an internal sonic physiognomy. Analyzing texts of Indian philosophy and ritual, and descriptions of lived experiences of religious sounds by sonic meditators, instrument craftsmen, and players, the chapter describes complex methods of constructing and experiencing the body. The body emerges as an auditory vessel, its inner space of breath paths and listening acumen embodying a distinctive religious capacity: to mirror and indeed become the devotional instrument. The author explores rare discourses of cultivation of the sound-perfect, meditative, tantric/yogic body and its relations to three instruments of substantial importance: the flute, the clay drum, and the conch.
Lifestyle and Diabetes Mellitus in Urban Communities
Authors: Herawati Cucu , Suryadilaga Teguh , Indragiri Suzana , Wahyuni Nuniek Tri , Sputriatin Sputriatin
Published: August 2023

People with Diabetes can risk heart, stroke, kidney, eye, and nerve complications. Factor risk of Diabetes mellitus caused by factors that cannot change and factored possible risks like style life. Objective study This is For analyzing factor risk style life with diabetes mellitus disease. Type research used survey analytics with a Cross-Sectional. Data collection method with interviews. Population in study This is patients taking care of the road diagnosed with DM in January 2022, with as many as 74 patients. The sampling technique used is Total Sampling data analysis using analysis univariate and bivariate. Research results obtained exist a significant relationship between activity physique (p-value = 0.000) and patterns Eating (p-value = 0.000) with DM disease; meanwhile, No There is a connection between style lifestyle smoke with DM disease (p-value = 0.097). Recommended public pre-elderly can reap style life Healthy life every day with regular physical activity, changing patterns of lousy eating, and stopping smoking from preventing DM disease
PENCAHAYAAN DAN KELELAHAN MATA PADA PEKERJA OFFICE PT. X
Authors: Hidayanti Aulia Nur , Lestari Putri Winda
Published: August 2023

Pekerjaan yang memerlukan ketelitian membutuhkan pencahayaan yang cukup, jika pencahayaan kurang maka dapat menyebabkan terjadinya kelelahan otot mata dan kelelahan saraf mata. Penelitian ini bertujuan untuk mengetahui apakah terdapat hubungan antara tingkat pencahayaan dengan terjadinya kelelahan mata pada pekerja office PT. X. Penelitian ini adalah penelitian kuantitatif observasional dengan cross-sectional study. Total sampling digunakan dalam penelitian ini sebanyak 37 orang. Data kelelahan mata diambil melalui pengisian kuesioner, data pencahayaan yang diambil dengan melakukan pengukuran menggunakan luxmeter. Data selanjutnya dianalisis secara univariat dan bivariat menggunakan uji statistik Chi-Square. Hasil penelitian menunjukan sebanyak 26 orang responden (70,3%) terpapar pencahayaan <300 lux. Responden yang mengalami kelelahan mata sebanyak 12 orang (32,4%). Hasil analisis bivariat menunjukan adanya hubungan antara tingkat pencahayaan dengan kelelahan mata (p=0,007).
HUBUNGAN POLA ASUH ORANG TUA DENGAN KEJADIAN STUNTING PADA BALITA USIA 24-59 BULAN DI WILAYAH KERJA PUSKESMAS SUMBANG II
Authors: Widianti Dian , Azizah Atika Nur
Published: August 2023

Stunting terjalin ketika pertumbuhan linier anak terhambat karena kelaparan terus-menerus atau faktor lingkungan yang kurang optimal. Jumlah anak pendek di Kabupaten Banyumas saat 2021 sebanyak 8527 (9,1%), seperti dilansir dari profil kesehatan daerah. Di Puskesmas Sumbang II, 14,2% dari 337 anak antara usia 0 dan 59 bulan dianggap pendek. Tingginya angka stunting di Kabupaten Banyumas sebagian disebabkan oleh pola asuh yang buruk. Tujuannya riset ini ialah supaya memahami bagaimana perbedaan pola asuh mempengaruhi angka kejadian stunting bagi anak berumur 24-59 bulan yang tinggal di Wilayah Kerja Puskesmas Sumbang II. Riset ini bersifat kuantitatif, dengan mempergunakan desain cross sectional dan analisis korelatif. Metode pengambilan sampel ialah multistage random sampling, dan total sampel ialah 72 orang. Mayoritas responden (91,6%) dengan keterampilan pengasuhan yang buruk mempunyai anak usia 24-59 bulan yang pendek atau sangat pendek. Dengan P-value (0,000 kurang daripada 0,005), riset ini menyimpulkan bahwa mayoritas orang tua masih melakukan pola asuh yang buruk. Saran untuk orang tua diharapkan melakukan pola asuh yang baik, Pola asuh yang baik telah terbukti mengurangi risiko stunting pada anak.
HUBUNGAN SELF EFFICACY DAN DUKUNGAN KELUARGA DENGAN SELF CARE PADA PASIEN DIABETES MELITUS TIPE II DI UPTD PUSKESMAS KADUDAMPIT KABUPATEN SUKABUMI
Authors: Suci Suci Fitriyani Amanda Kartini , Dewi Rosliana , Liawati Nunung
Published: August 2023

Diabetes mellitus is a metabolic illness which is a world issue. The occurrence rate will increment until 2045. This study expects to break down the connection among family support and self-efficacy with self-care of oneself on the patient with DM. The examination configuration utilized quantitative exploration with a cross sectional review. The populace in this research is all DM patients  visited in puskesmas for quite some time, to be specific 51 patients and the example in this study were all populaces. The most effective method to take tests in this review utilizing unintentional examining. The information investigation utilized was bivariate and univariate examination with Chi-Square. The outcomes show that most of self-efficacy is high, family support is strong, and self care of oneself is great. In light of the consequences of the connection test, it was acquired that the huge worth of the connection among self-efficacy and self care of oneself was sig = 0.010, family support with taking care of oneself was sig = 0.005. The end in this examination is that there’s a connection among self-efficacy and family support with self care of oneself in Type II DM  patients at the UPTD Puskesmas Kadudamit, Sukabumi Regency.