bluuebunny/crossref_metadata_2025 huggingface dataset:
The Review of Research on 3D Model Reconstruction Based on Point Cloud Data
Authors: Shi Dingnan
Published: August 2024

Point cloud-based 3D modeling is a cutting-edge technology that leverages point cloud data from sensors like lidar and cameras to recreate detailed 3D models of objects and environments. This paper serves to outline the fundamental route, key principles, and prevalent methods within this field, with a specific emphasis on the preprocessing registration techniques for point cloud data and the advancements in 3D model reconstruction technologies.Furthermore, the article will delve into foundational 3D reconstruction methodologies, categorizing them into optimization-driven approaches and interpolation or fitting strategies. By categorizing these methods, it becomes possible to address the limitations and challenges associated with each algorithm and propose potential enhancement strategies to overcome these obstacles.By refining existing techniques, developing novel methodologies, and enhancing computational efficiency, the future holds promise for significant advancements in the realm of 3D modeling through point cloud data. In the conclusion, we provide a summary and outlook for the entire paper.
Comparison of AlexNet and ResNet Models for Remote Sensing Image Recognition
Authors: Zhang Wenxuan
Published: August 2024

Remote sensing image recognition is an important direction in remote sensing data processing. Among them, deep neural networks have achieved results far beyond traditional methods on many challenging image datasets in remote sensing images. There are various network models for convolutional neural networks, for better remote sensing image recognition, this paper has selected the AlexNet model and ResNet model for comparison to select the network model with more accurate results. After running, it found that the accuracy is 0.6479 on the ResNet model and 0.6023 on the AlexNet model, the dataset performs better on the ResNet18 model, to further elucidate this result, it analysed the difference in accuracy between the two models. It was found that the use of techniques such as learning residuals, jump connections, and batch variance in the ResNet18 model reduced the problem of gradient vanishing during training, while the deeper layers and higher number of parameters also led to higher accuracy of the ResNet18 model.
Research on Modeling Quantum Computing Applications in Mine Equipment Configuration and Operations
Authors: Jiang Tao , Fang Ni , Deng Mengyao
Published: August 2024

This paper explores the optimization of mine equipment configuration and operations using quantum computing and the Quadratic Unconstrained Binary Optimization (QUBO) model. It highlights quantum computing's ability to enhance problem-solving efficiency and solution quality, especially for complex and multivariable optimization problems, compared to traditional methods.The research focuses on two main areas: optimizing equipment configuration and scheduling operations. For equipment configuration, potential schemes are analyzed for complexity and cost-effectiveness, with validation on quantum hardware. For operation scheduling, equipment data is analyzed to create an optimization model, demonstrating improved efficiency through simulations.The study concludes that quantum computing significantly enhances efficiency and solution quality for mine equipment management, proving its superiority over traditional methods.
Research on Predicting the GBP/USD Exchange Rate Based on BP Neural Network
Authors: Wu Zulin , Xie Yihan
Published: August 2024

As global base currencies, the fluctuations in the prices of the pound and dollar have an impact on the global economic situation. The exchange rate of currency not only affects the decline of the economy but also affects the stability and development of political and trade relations in various countries. Due to the numerous and non-linear factors that affect the currency exchange rates between the two countries, the accuracy of existing exchange rate prediction research is still relatively low. This experiment conducted a study on the prediction of the pound to dollar exchange rate, to help deepen the understanding of the operating rules of the international economy and financial markets and take corresponding measures. This experiment selected data on the GBP/USD exchange rate from May 12, 1993, to March 1, 2024, and used MATLAB to establish a Back Propagation (BP) neural network model to study the GBP/USD exchange rate. This article optimizes the prediction performance of exchange rates in this experiment by changing the number of hidden layer nodes as much as possible. The results show that the BP neural network in this experiment has a relative prediction error of less than 0.35 for the pound-to-dollar exchange rate, and has a good fitting effect.
Enhancing Cognitive Signal Processing: Advanced CNN Architectures for EEG-Inferred Digit Recognition
Authors: Zhang Xiongzhewen
Published: August 2024

The EEG, or Electroencephalogram, is a non-invasive tool that records electrical activity along the scalp, offering a window into the complex workings of the brain. By analyzing these signals, researchers can gain insights into cognitive processes such as attention, memory, and decision-making. This investigation utilizes Convolutional Neural Networks (CNN) for the classification of EEG signals into numerical digits, tackling the challenge of deciphering cognitive states via non-invasive techniques. The core of the study is the utilization of extensive datasets and sophisticated CNN models to evaluate the performance of consumer-grade EEG headsets in brain activity interpretation. The results showcase high accuracy in numerical cognition identification, demonstrating the robustness of the methods used and suggesting their wider applicability for cognitive state analysis. Overall, the research conducted in the field of EEG-based diagnostics represents a significant milestone in the cognitive technologies industry. Its contributions to the field are numerous and far-reaching, setting a new benchmark for subsequent investigations into this exciting domain. As we continue to explore the potential of EEG technology, we can look forward to a future where cognitive technologies become increasingly personalized, accurate, and effective.
Automation and the Workforce: Navigating the Future of Employment
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter explores the transformative impact of automation on the workforce, examining how advanced technologies like robotics, artificial intelligence, and the Internet of Things are reshaping employment across various industries. It delves into the dual forces of job displacement and creation, highlighting the changing roles and skills required in an automated economy. The chapter also discusses the critical role of education, reskilling, and lifelong learning in preparing workers for the future job market. Additionally, it addresses the challenges businesses face when integrating automation, the importance of government policies in supporting workers during this transition, and the emerging trends that will shape the future of employment. By providing insights into the strategies needed to navigate these changes, the chapter offers a comprehensive guide to understanding and adapting to the evolving landscape of work in the age of automation. Keywords: Automation, Workforce Transformation, Job Displacement, Job Creation, Reskilling, Upskilling, Lifelong Learning, Human-Machine Collaboration, Future of Work, Gig Economy, Flexible Work Arrangements, AI in Employment,Economic Impact of Automation, Education and Training, Policy and Regulation.
Strategic thinking as a potential factor in the growth of companies in the dairy sector
Authors: Vera Pirela Carlos , Olmedo Plata Aneleth , Lozano Hernandez German
Published: August 2024

The article refers to how in a competitive and changing environment, dairy companies need to implement effective strategies to ensure their growth and sustainability, emerging as a crucial factor for the business success of the sector. In this same scenario, strategic thinking implies the ability to analyze the environment, identify opportunities and threats, and formulate action plans to achieve long-term objectives. This research focused on a quantitative, field, non-experimental methodology. Descriptive statistics were used to tabulate the information; the results of the study reveal a strong commitment of the company to identify market opportunities and reinforce its competitive strengths. The indicators associated with these areas showed a high level of presence within the organization, which shows a solid foundation for future growth
Benefits and challenges of artificial intelligence in the Colombian health system
Authors: Valencia Sinisterra Diana , Barrientos Kelly Johana , Llanes Villota Maria Angelica
Published: August 2024

This study explored the impact of artificial intelligence (AI) on the Colombian healthcare system, focusing on its potential to improve diagnosis, treatment, and resource management, the methodology included a literature review and case study analysis in rural and urban areas, findings revealed that AI can enhance the accuracy and speed of clinical decision-making, address the lack of specialist access in remote areas, and personalize medical treatments. However, significant challenges were also identified, such as insufficient technological infrastructure, the need for adequate health personnel training, and ethical and data protection concerns. It was concluded that to maximize the benefits of AI and minimize its risks, careful planning, adequate investments in infrastructure and continuous staff training, as well as robust ethical and legal regulation, are essential. Additionally, the importance of designing AI implementation policies that consider and address existing inequalities in access to healthcare services was emphasized
Transforming the Salitre campus into a smart campus: proposal of smart initiatives for the Gerardo Barrios University of El Salvador
Authors: García Espinoza Gisela Yasmín
Published: August 2024

This study proposes a set of initiatives to transform the Salitre campus of the Gerardo Barrios University in San Miguel, El Salvador, into a Smart Campus inspired by Smart City concepts. Through a documentary research, a detailed diagnosis of the campus and a SWOT analysis, several proposals were defined grouped in six axes: Smart Government, Smart Environment, Smart Living, Smart Economy, Smart People and Smart Mobility. The viability of the initiatives was evaluated considering their acceptance by the authorities and stakeholders, as well as a feasibility study. The results show that the transformation of the Salitre campus into a Smart Campus may be possible and would bring multiple benefits in terms of efficiency, sustainability, innovation and quality of life for the university community. The study establishes the basis for future projects and research, promoting digital and sustainable transformation in the educational and community environment of El Salvador.
Attachment Styles in Romantic Relationships and Perceived Support in Postpartum Couples
Authors: McLeod Saul , Wearden Alison , Taylor Peter , Oaks Lucy
Published: August 2024

The transition to parenthood is a significant life event that presents various challenges. Adequate support for both parents is crucial, as insufficient support can lead to negative outcomes. While insecure attachment has been identified as a risk factor during this period, there is limited research on how dyadic attachment styles influence perceptions of received support. This study investigates the relationship between momentary perceptions of support matching and attachment styles among parental dyads using the experience sampling method (ESM) to reduce retrospective bias. For seven days, 40 mothers of infants aged 3-12 months completed daily self-report measures relating to desired and received support from their romantic partner. Multilevel analyses revealed that securely attached mothers perceived greater support matching compared to those with insecure attachment styles. The attachment style of the support provider (father) did not significantly affect the mother’s perceptions of support adequacy. These findings highlight the importance of attachment security in shaping support perceptions and suggest that attachment-based interventions could improve support experiences for new parents. Future research should explore the nuances of attachment pairings and their impact on support dynamics within couples. Interventions following childbirth should focus on improving maternal attachment security to enhance support matching and well-being for new parents. Keywords: attachment style, romantic attachment, postpartum, support
Company Performance Appraisal Analysis Using Balanced Scorecard At Madjatea Shopperba
Authors: Fauzi Haris , Hernita Nita
Published: August 2024

The problem that occurred at MadjateaMajalengka Department Store was a decrease in sales which resulted in a decrease in store turnover and income. This decrease is an evaluation of the use of the Balance Scorecard performance measurement method that is being tried to be applied.This study aims to determine the performance analysis of MadjateaDepartment Stores using the Balance Scorecard method. The variables used are a financial perspective, customer perspective, internal business perspective and learning and growth perspective. This study uses primary data from questionnaires and secondary data from MadjateaToserba's financial reports. The method used in this study is a survey method with a descriptive and verification approach. The sampling technique used is saturated sampling technique with a total of MadjateaDepartment Store employee respondents. The analytical tool used in this study is the Balanced Scorecard method with 4 approaches.The results of this study indicate that the performance of Madjatea Department Stores using the Balanced Scorecard method found that the financial perspective is categorized as healthy, the customer perspective is categorized as unhealthy, the internal business perspective is categorized as unhealthy and the learning and growth perspective is categorized as unhealthy. So it is necessary to increase the 3 unhealthy aspects by using the Madjatea Department Store performance measurement through the balanced scorecard method.
Environmental Sustainability And Economic Growth: Policy Implications For Developing Countries
Authors: Utomo Bekti , Riatmaja Dodi Setiawan , Aninam Johny , Wahyudi Fajar Satriyawan
Published: August 2024

Environmental sustainability and economic growth are often considered to be two conflicting goals, especially in developing countries that face significant challenges in managing natural resources while pursuing economic growth. This research explores the complex relationship between environmental sustainability and economic growth in developing countries, with the aim of identifying policies that support sustainable economic growth. The methods used include literature reviews and thematic analysis to identify key patterns and relationships in existing studies. The research results show that although economic growth is often accompanied by increases in resource consumption and pollutant emissions, developing countries can achieve sustainable growth through strengthening environmental regulations, investing in green technologies, and increasing public awareness. Economic diversification, such as the development of sustainable tourism and organic agribusiness, can also reduce pressure on natural resources and create new economic opportunities. The proposed policy implications include increased environmental regulations, support for green technologies, and public education efforts to support environmentally friendly practices, with an integrated approach that ensures a balance between economic growth and environmental protection.
Prediction of death causes using regression models and moving averages
Authors: Abbas Irfan , Muneer Umair
Published: August 2024

Savings lives of fellow citizens is the first priority of all governments in the world. Regression analysis is used to engine knowledge generation to predict the future threats and a massive support for policy makers and health organization for providing health care facilities. In this paper, our purpose is to address the causality which is causing death in France from 2001-2008[1]. The rapidly increasing data requiring the earlier change before the major human loss. Implementation is suggested through data filtering by regression analysis and moving averages for each cause. Also done the prediction model comparison by implemented linear and quadratic regression models. The storage and retrieval of huge data will enable to get meaningful conclusion out of that data to prevent death ratio in future.
Develop deep learning software with automated filtering of email phishing to counter phishing attacks
Authors: Xuan Cheah Khai , Kasmin Intan Farahana , Amin Salmiah , Zainal Nur Khairunnisha
Published: August 2024

Tech-savvy con artists and identity theft criminals carry out email phishing attacks online. To fool you into giving critical information like bank account passwords and credit card details, they employ spam, bogus websites that seem just like legitimate websites, email, and instant chats. Therefore, this research paper is to develop deep learning software with auto-filtering the phishing email from the sender for prevents unauthorize spoofing and using the survey method to collect the respondent perspective about the phishing email with open and closed questions. Therefore, by using this system will help the users to protect their confidential data.
Cyber Security Implementation in Hospitality Industry
Authors: En Chai Ming , Kasmin Intan Farahana , Zainal Abidin Zety Marlia , Vasudavan Hemalata
Published: August 2024

The widespread use of IoT in the hospitality industry has led in increase in the number of cyber security challenges faced by the hospitality industry, as well as the sophistication of the cyber-attacks. A lot of hotel organization do not conduct security audits or do so infrequently, exposing customers and the organization to the risk of data breaches. This research is aimed to identify the cyber security challenges faced by the hospitality industry and propose an applicable cyber security solution to prevent cyber security attack for hospitality industry. To ensure the reliability of this research, interviews and surveys data collection methodologies have been conducted with IT experts in the hospitality industry, hospitality staff, and hotel guests. The interviews will be conducted as one-by-one structured interview with cyber security experts from the hospitality industry. A large number of hospitality staff and customers will be interviewed to determine how well they understand cyber security in the hospitality industry by using survey data collection method. At the end of this research the cyber security challenges faced by hospitality industry, the advance cyber security technologies and the solution of handling hospitality industry cyber-attack have been identified. An overview of Hospitality Industry Cyber Security System has been proposed.
Credit Monitoring Application: Protect Children from Child Identity Theft
Authors: Liang Chua Zi , Kasmin Intan Farahana , Zainal Abidin Zety Marlia , Vasudavan Hemalata
Published: August 2024

Data breaching is a security infringement widely used by the hackers to gain certain information or data with unauthorized access which threaten countless organizations and individuals. It has been an unpreventable cyber threat since 2005 by grabbing the attention of the public to start concerning data breaching but limited solutions are provided to prevent this cyber-attack. Furthermore, one of the most crucial types of data breaching is child identity breach. At this point, there was still no solution to stop child identity theft, parents and guardians are only able to minimize the risk of their child’s identity breach. In this proposal, a reliable Children’s Data Breaching solution is being proposed to protect the children by enhancing the Credit Monitoring Applications to better protect the children’s identity under 18.
RFID Security Issue That is Implemented into Malaysia Toll Collection System
Authors: Onn Goh Chei , Kasmin Intan Farahana , Zainal Abidin Zety Marlia , Vasudavan Hemalata
Published: August 2024

RFID was invented and use by lots of user and industries. There is a security issue which outsider can get information from the RFID tag and it need to be solved. A survey will be conducted to get an idea on how many users are aware of the security issue of RFID technology. With the help of a new system, the owner can deactivate their RFID tag so it will show non information. Owner of the RFID tag should always check their bank balance and other information that is encoded in their RFID tag to make sure no information is leak to others.
Gamified Tailored Roleplay Story-based Phishing Awareness Training
Authors: Wijaya William , Kasmin Intan Farahana , Zainal Abidin Zety Marlia , Vasudavan Hemalata
Published: August 2024

Nowadays, the world has transformed into a digitalize era where more and more people are getting access to the internet. Simultaneously, the number of cybercrimes is increasing as well. One of the top number of cases is phishing attack. To tackle this problem, phishing awareness trainings were developed, but they were not effective. This research aims to propose a solution using gamification, AI, and roleplay story element to conduct the training. To carry out the research questions, quota sampling would be used to discover the appropriate game components and context scenarios, and snowball sampling would be used to get suitable data which could be used for learning analytics to craft new scenarios. To sum up, phishing attack is one of the most concerning issues in this digital era and effective solution is yet to be found. Thus, this paper aims to propose a solution to tackle this problem.
Performance Evaluation of IoT Routing Protocols
Authors: Siddiqui Ateefa Rehan , Kuruvikulam Chandrasekaran Arun
Published: August 2024

Just as the tremendous measure of new information rising as the world modernizes. The significance of IoT- empowered advancements is no less significant in different areas. The IoT insurgency is changing the whole range of processing- based administrations. Another influx of quick foundation advancement is clearing the globe, including basically everything advanced server farms, supercomputers, groups, installed frameworks, servers, and organizations to control matrices, sensors, gadgets, apparatuses, cell phones, and instruments. The IoT worldview makes actual items with detecting capacities, incorporating those with labels, tech-savvy meters, buyer electronic gadgets, wellbeing sensors and gadgets, and portable things like advanced mobile phones, robots, vehicles, and home machines. The paper examines the different routing protocols available for ensuring the smooth communication of items in the Internet of Things, the simulations of the protocols discussed in this paper, and the pros, disadvantages, and problems mentioned earlier.
Supply Chain in Non-Profit Organizations: The Impact of Data Analytics
Authors: Barret Christopher Adler , Gonzalez Marcus Roman , Gomez Valentina Isabella
Published: August 2024

This paper aims to understand the nomological network of associations between collective and big data analytics in cultivating resilient humanitarian relief supply chains. Methodology / Approach the authors formulate a research design grounded in literature as well as test. The hypotheses utilizing survey data obtained from informants at human aid organizations in Europe and Africa. The findings show that organizational is crucial to enabling resilient humanitarian relief supply chains, instead of just big data analytics. This study is the first to look at organizational as well as big data analytics within the context of humanitarian aid supply chains.
Effectiveness of UK Legislation and Management in Producing Nature Conservation Outcomes
Authors: Bridger Molly Anne
Published: August 2024

National Nature Reserves (NNRs) are a form of nature conservation management using protected areas to improve specific site features of wider biodiversity. This study focuses on the Purbeck Heath NNR management aims to restore the natural habitat and increase overall biodiversity through landscape-scale management. This study investigated whether landscape-scale management used within the protected areas will benefit overall biodiversity and fulfil legislation. Sites observations were used to assess public compliance in accordance with the legislation and management measures of the specific site, whilst personal communications were carried out to gain representations of what key ecological measures are currently used on site. Bayesian Belief Networks were used to evaluate how different designated features within NNRs will continue to perform under current management methods. Results showed that protected designated features within the reserve would improve under the landscape-scale management of the Purbeck Heath NNR with ecosystem services, biodiversity, protected target species, and protected target habitat likely to increase despite climate change likely to increase. Discussions showed that the main conservation outcome is to increase landscape connectivity, climate resilience and overall biodiversity by placing nature first. Overall, the Purbeck Heath NNR and other super NNRs have the potential to fulfil the legislation aims set out under the various designations of Special Protected Areas, Special Areas of Conservation, Sites of Special Scientific Interest, and Ramsar sites.
Credit Rating Prediction Using Different Machine Learning Techniques
Authors: Aiyegbeni Gifty , Li Yang , Annan Joseph , Adebayo Funminiyi
Published: August 2024

Credit rating prediction is a crucial task in the banking and financial industry. Financial firms want to identify the likelihood of customers repaying loans or credit. With the advent of machine learning algorithms and big data analytics, it is now possible to automate and improve the accuracy of credit rating prediction.  In this research, we aim to develop a machine learning-based approach for customer credit rating prediction. Machine learning algorithms, including decision trees, random forests, support vector machines, and logistic regression, were evaluated and compared in terms of accuracy, precision, and AUC. Feature selection was also performed to analyze the importance of different features in predicting credit ratings. Findings suggested that status, duration, credit history, amount, savings, other debtors, property, and employment duration are the most important features in predicting credit ratings. Results showed that the support vector machine algorithm did best in predicting bad credits, achieving an accuracy of 79.7%, AUROC of 0.76, and a precision of 0.88. After optimization, an AUROC of 78% was obtained. This is a 78% accuracy for properly identifying bad credits.  This research demonstrates the potential of machine learning algorithms for customer credit rating prediction and could have significant implications for the banking and financial industry by enabling more accurate and efficient credit rating predictions and reducing the risk of defaults and financial losses.
Using Supervised Machine Learning Models and Natural Language Processing for Identification of Fake News
Authors: Mohan Sidarth , Assi Jolnar , Mohammed Ammar H
Published: August 2024

Social media has gained popularity over the last decade due to its ease of access and providing large amount of information to people. In seconds, users are able to access information from social media related to politics, life-style, science and money other fields. However, data obtained from social media platforms represent a mixture of fake and real news. Fake news are in-tended to deceive people and change their attitudes and beliefs. Machine learning algorithms have shown successful in classifying real from fake news. Nonetheless when applying machine learning models in this context related to limitations in the dataset type, balance or skewness. Hence, data pre-processing is essential prior to application of machine learning models. Therefore, this work evaluated the use of supervised machine learning models with different data pre-processing approaches for classification of fake news obtained from social media platforms. Different pre-processing techniques have been applied related to feature extraction and feature selection alongside four machine learning models being logistic regression, decision trees, random forest and extreme gradient boost. The findings showed that random forest and extreme gradient boost with bi-gram feature extraction and chi-squared feature selection showed the best performance. Future work involves using the proposed model to detecting fake news in different con-text and different languages.
DATA AUGMENTATION FOR NATURAL LANGUAGE PROCESSING
Authors: Gupta Parul , Mahmood Maha
Published: August 2024

Recent years have seen a rise in the use of data augmentation approaches in natural language processing (NLP) to create more trustworthy models. Data augmentation has recently received a lot of interest in NLP due to new aims, more work being done in lower source domains, and the popularity of large-scale neural networks, which need a lot of training data. Despite this recent development, there hasn't been much research done in this area; this may be because the linguistic data presents some challenges. In this paper we compared four data augmentation (easy data augmentations (EDA), backtranslation, Mix-up and generative models like GPT-2 and BERT) approaches on two datasets for the NLP tasks of sentiment classification and question classification and used accuracy, precision, recall and f1- scores as evaluation metrics. We showed how not only accuracy, but other evaluation metrics are also required to choose the best model especially when the dataset is imbalance. We also show that these data augmentation approaches perform well only in low-data regime and the evaluation metrics for these augmentation techniques starts to get hurt when the training data is increased. Further we also concluded how backtranslation augmentation method performance depends on the language used for translation. Based on the findings, we made several recommendations for potential future work for the researchers to work on in the future.
Predicting Crude Oil Price Using Time Series Statistical Modelling Techniques
Authors: Gupta Sunny Raj Pradeep Kumar , Coombs Thomas
Published: August 2024

One of the important topics in Global Economy is crude oil prediction. Nonetheless, crude oil prediction is related to many factors that play a role in it and include geological factors, production, technical etc…Accurate prediction of crude oil is significant considering all the previous factors. However, these factors are volatile and vary under different conditions. Therefore, this study proposes using machine learning models for time series predictions of crude oil. More specifically, three types of convolutional neural networks were used being autoregressive integrated moving average (ARIMA), seasonal autoregressive moving average (SARIMA) and long short term memory (LSTM). The models were applied to three datasets of crude oil including Crude Oil Daily Price Data, Crude Oil Weekly Inventory Data and Crude Oil Monthly Production Data. Data analysis was conducted using Python. The outcomes of the predictions showed that LSTM had higher accuracy and prediction than ARIMA and SARIMA. The LSTM daily model achieved a MAPE score of as low as 0.087. Future research involve more reliable production data and include more reliable input parameters to improve accuracy.
Student Perceptions of Flexible Deadlines
Authors: Kennette Lynne N. , Rivers Michelle
Published: August 2024

Instructors’ course policies have an important impact on student success in our courses, as well as their perceptions of instructors. One such course policy, which is the focus of this descriptive study, is that of assessment deadlines, more specifically, the various permutations of flexible deadlines. These might include automatic extensions, short or long extensions with or without a penalty, open deadlines for submissions, or a bonus point as an incentive for meeting the deadline. In the present study, we asked students to evaluate these submission deadline policies and how they might affect their wellbeing, procrastination, and perceptions of their instructor. Although they report encountering them most frequently, students don’t perceive extensions with a reduction in grade (e.g., 10% per day) as helpful for their learning and would prefer automatic non-punitive extensions to help support their success and wellbeing. Additionally, students reported that they would have a more positive view of their instructor (nicer and cares about their success) if they had a flexible deadline policy and that it would increase their satisfaction with both the course and instructor. Surprisingly, the largest number of students indicated that their preferred flexible deadline policy would be to receive a bonus for submitting it on time. Implications for policy and student success are discussed. The authors recommend that faculty who use hard/rigid deadlines consider adopting flexible deadlines to better support student success.
Impact of climate change on forest ecosystems in Western Polissia
Authors: Yanitskyi Volodymyr
Published: August 2024

Climate change is affecting forest ecosystems, leading to changes in the distribution of tree species, ecological relationships, and the functioning of forest plant communities. The study aimed to analyse the impact of climate change on forest ecosystems in the Western Polissia of Ukraine. To achieve this goal, in 2005-2023, the State Enterprise “Gorodetske” Research Farm studied the current state of the forest ecosystem, analysed the impact of climate change on it, and conducted detailed forest pathology reconnaissance surveys of the stands. Biodiversity indices were also determined, and the rate of degradation and death of forests was analysed using field data and satellite images. The study determined that climate change in the Western Polissia Region has a significant impact on forest ecosystems, causing changes in forest composition, the spread of diseases and pests, and a decrease in natural biodiversity. The study established that to ensure their sustainable functioning and conservation, it is necessary to take specific measures, including early diagnosis of the state of forests, adaptation to climate change, prevention of forest fires, and use of forest resources based on the principles of sustainable forestry. Implementation of such strategies can contribute to the preservation of ecological diversity and sustainable development in the region for years to come. The results obtained are of great importance for environmental management and conservation of natural resources in the Western Polissia Region, as they provide an opportunity to identify specific strategies and measures for the adaptation of forest ecosystems to climate change and other environmental challenges
The efficiency of organic pollutants degradation in the process of anaerobic digestion of feedstocks with different origin
Authors: Paramonov Andrii , Ablieieva Iryna , Vaskina Iryna , Lysytska Anna , Makarenko Nataliia
Published: August 2024

The study of the degree of degradation of pollutants in the process of anaerobic digestion and their content in digestate is an urgent scientific issue driven by the necessity to confirm the safety of using this product as a biofertilizer to improve the quality of agricultural land. The aim of the article was to determine the efficiency of the degradation of pollutants in the process of anaerobic digestion with the use of various additional treatment methods. A meta-analysis was conducted to establish the patterns of pollutant degradation during anaerobic digestion under different conditions and with the application of additional process intensification technologies (ultrasound, the use of chemical reagents and carbon cloth, interspecies electron transfer, immobilisation of microalgae, etc.). The reliability of the selected hypotheses and the statistical significance of the obtained data were determined using statistical analysis methods. It was found that additional treatment methods allow to increase in the efficiency of degradation of organic pollutants in mixed organic waste by 180% (direct interspecies electron transfer), in animal waste by 115% (alkaline pre-treatment), and in wastewater sludge by 55% (by treating them in a thermophilic aerobic reactor together with anaerobic digestion). The efficiency of the immobilisation of microalgae technology, which provides for the removal of pollutants from the liquid fraction of digestate, is 135%. The presence of a synergistic effect of additional methods of feedstock treatment has also been established, which is illustrated by an increase in the amount of methane production by 98.88-261.56%, depending on the type of waste and the treatment method. The results obtained prove the high efficiency of additional treatment technologies. The practical significance of the results obtained lies in the proven synergistic effect of using additional methods of treatment of feedstocks and digestate due to an increase in the level of degradation of organic pollutants with the production of environmentally safe fertilizer and an increase in biogas yield
Analysis of alternative approaches to stormwater management and prospects for their implementation in Ukraine
Authors: Kravchenko Maryna , Tkachenko Tetiana
Published: August 2024

Due to aging infrastructure, frequent wastewater network overflows, and potential charges for stormwater disposal, there is a need to find alternative approaches to stormwater management. Rain gardens and rainwater harvesting systems are widely used in local areas such as private homes, small businesses, and parking lots. Therefore, the aim of this study was to conduct an economic analysis of two alternative solutions – a storage tank with water reuse and a rain garden. The cost-benefit analysis was based on an evaluation of the effectiveness of each approach in reducing stormwater runoff volume and decreasing stormwater disposal fees in the wastewater network. The precipitation regime used for the calculations was selected based on data from 2014 to 2023 for the Kyiv. In the study, technical and economic aspects were considered as key factors in the decision-making process. The results of the economic sustainability analysis of both options using an example of an impermeable surface showed that both systems have an effective service life of approximately two years. The storage tank is an effective solution, reducing stormwater overflows and allowing the collected water to be reused for various purposes. However, the costs of construction and maintenance of the tank exceed the savings on stormwater disposal fees to the sewer system by almost twice, therefore the project is not economically viable in the initial period. The implementation of a rain garden design to reduce stormwater disposal fees to the wastewater network is the most cost-effective solution. Assuming that the effective use period of a rain garden design is 8-10 years, the accumulated savings on tariffs can range from €606.8 to €848.2. The introduction of financial incentives will help promote the implementation of alternative stormwater control solutions, leading to a range of environmental and economic benefits, such as reducing the impact of stormwater on the environment, protecting water resources, and potential savings in the construction and management of stormwater systems
SOLUTION WAYS OF NON-STANDARD EXERCISES IN PRIMARY CLASS MATHEMATICS LESSONS
Authors: İsrafilova Günel
Published: August 2024

The article mentions different ways of solving non-standard studies in mathematics classes of primary classes. Students' heuristic activities prevail in the process of solving non-standard problems. Currently, such issues are already reflected in mathematics textbooks in accordance with the curriculum requirements.
PERANCANGAN SISTEM INFORMASI APLIKASI RENTAL MOBIL DENGAN METODE RAPID APPLICATION DEVELOPMENT BERBASIS MOBILE
Authors: Virtalioka Sheva , Vernanda Dwi , Rifal Nurpazri Adifa
Published: August 2024

Di era modern ini, teknologi informasi menjadi sangat penting dalam memberikan solusi inovatif pada industry rental mobil. Untuk memenuhi kebutuhan efisiensi, keamanan dan pengelolaan informasi yang optimal, pengembangan sistem informasi aplikasi rental mobil berbasis mobile merupakan pilihan yang paling penting. Dalam penelitian ini, kami menerapkan metodologi Rapid Application Development (RAD) sebagai pendekatan pengembangan sistem yang memungkinkan proses yang cepat dan dapat direproduksi. Fase penelitian meliputi penilaian kebutuhan, desain sistem, pengembangan aplikasi berbasis prototipe, dan pengumpulan umpan balik pengguna secara berkelanjutan. Data diperoleh melalui wawancara dengan perusahaan rental mobil dan pengguna aplikasi, serta observasi langsung di lapangan. Hasilnya adalah aplikasi berbasis mobile yang memungkinkan pemesanan dan pengelolaan kendaraan secara efisien, meminimalkan kemungkinan penyalahgunaan mobil sewaan dan menjamin keamanan mobil sewaan. Perancangan antarmuka, diagram aliran data, dan perancangan basis data menjadi dasar pengembangan aplikasi ini. Aplikasi baru yang dikembangkan diharapkan dapat berkontribusi dalam memperlancar proses bisnis di industri rental mobil.
El uso del tiempo en las actividades cotidianas y la desigualdad de género
Authors: Pech Quijano Adrián Eduardo , Pech Campos Susana Candelaria
Published: August 2024

El tiempo total de trabajo se refiere a: la suma de las horas dedicadas a las actividades de apoyo a personas dependientes de los hogares y las tareas domésticas no remuneradas, y el número de horas semanales de trabajo remunerado que se registran en las cuentas nacionales. El tiempo total dedicado al trabajo ocupa gran parte de la vida de las personas, y en el caso de las mujeres representa más del doble respecto a las horas dedicadas a actividades personales, y de un poco más de la mitad respecto a las horas dedicadas a convivencia y entretenimiento, según datos de la Encuesta Nacional sobre Uso del Tiempo (ENUT) del año 2019. Estos datos permiten visualizar las restricciones impuestas por las actividades no remuneradas en los hogares, en el caso de las mujeres quienes tienen mayor número de horas de trabajo a diferencia de los hombres, aunado a esto, afrontan limitaciones no sólo en el uso del tiempo, sino en temas culturales y espaciales para emplearse en un trabajo remunerado. La realización del trabajo doméstico no remunerado se nutre por estereotipos de género, donde la mujer es la principal responsable de la crianza de los hijos, del cuidado y apoyo de los integrantes más vulnerables de la familia, así como de las tareas domésticas, en tanto el hombre se enfoca en su rol masculino de proveedor de la familia, por lo que en este artículo se analiza el uso del tiempo de las mujeres y su impacto económico en la desigualdad de género en el estado de Campeche.
Evaluación de la producción textual en los niños y niñas del grado tercero de Cali, Colombia
Authors: Paz Sánchez Mirna Ruth
Published: August 2024

En este artículo se plasma una investigación tendiente a desarrollar una estrategia evaluativa que mejore la producción textual de los estudiantes de grado tercero de la educación básica primaria. Por este motivo, su objetivo es: diseñar una propuesta para evaluar la producción textual de los niños y niñas del grado tercero de la básica primaria en la institución educativa Vicente Borrero Costa, sede Presbítero Eloy Valenzuela de la ciudad de Santiago de Cali. La investigación se desarrolla dentro de un proceso de tipo cualitativo y a su vez descriptivo, es en un sentido socio constructivista de tipo participativo (docentes, estudiantes, padres de familia y contexto institucional) y emplea como instrumentos para obtener la información: la encuesta para los docentes y padres de familia y las rejillas de observación con los estudiantes. Todo esto enmarcado dentro de una estrategia metodológica denominada secuencia didáctica. Teniendo como fundamento la secuencia didáctica, los resultados obtenidos con las rejillas tanto de entrada como de cierre, demuestran cómo el proyecto cumple con los objetivos propuestos y da respuesta a la pregunta planteada. Esto significa que su aplicación desarrolla una evaluación que conlleva al mejoramiento de la producción textual en los estudiantes de tercer grado de la básica primaria.
UPAYA MENINGKATKAN KINERJA WAITER ATAU WAITRESS DALAM MENUNJANG KEBERHASILAN OPERASIONAL FOOD AND BEVERAGE SERVICE TERHADAP DAMPAK PENJUALAN DI HOTEL GRAND KEISHA YOGYAKARTA
Authors: Dhian Anggraini Fera , Beru Utami Lulux
Published: August 2024

Dalam era globalisasi, khususnya dalam tatanan perekonomian dunia, telah mendorong lahirnya organisasi-organisasi pasar bersama (pasar bebas) yang artinya setiap negara akan menjadi ajang persaingan bangsa-bangsa lain untuk dapat bersaing diperlukan Sumber Daya Manusia (SDM) yang memiliki keahlian profesional. Keahlian profesional yang harus diakui pada dasarnya mengandung unsur ilmu pengetahuan, teknik dan kiat. Unsur kiat menjadi salah satu faktor utama penentu kadar profesionalan seorang hanya dapat dikuasai melalui cara mengerjakan langsung pekerjaan pada bidang profesi itu sendiri, karena itulah tumbuh suatu ukuran keahlian profesional berdasarkan jumlah pengalaman kerja. Berdasarkan Keputusan Menteri Parpostel no Km 94/HK103/MPPT 1987, pengertian hotel adalah Salah satu jenis akomodasi yang mempergunakan sebagian atau keseluruhan bagian untuk jasa pelayanan penginapan, penyedia makanan dan minuman serta jasa lainnya bagi masyarakat umum yang dikelola secara komersil. Di dalam sebuah hotel terdapat departemen-departemen penting yang dapat menunjang operasional hotel berjalan dengan baik, diantaranya adalah Front Office Departement, Housekeeping Departement, Engineering Departement, Food and Beverage Product, Food and Beverage Serivice Departement, dan lain-lain.
Food and Beverage Serivice Departement adalah bagian Food and Beverage yang langsung berhubungan dengan tamu, salah satunya adalah pada bagian restoran hotel. Restoran merupakan suatu tempat yang dikelola secara komersil yang menyediakan pelayanan berupa makanan dan minuman. Di dalam sebuah restoran, waiter dan waitress menjadi unsur pendukung utama dalam kegiatan operasionalnya. Oleh kare itu pada departemen ini dituntut untuk dapat memberikan pelayanan secara baik dan profesional agar dapat memuaskan para tamunya. Food and Beverage dan outlet-outlet dari Food and Beverage juga mempunyai fungsi sebagai penambah pendapatan hotel diluar dari pendapatan penjualan kamar. Untuk menunjang kelancaran operasional di Food and Beverage Service Department Hotel Grand Keisha Yogyakarta menyediakan peralatan-peralatan modern dan lengkap sesuai kegunaannya. Selain itu Hotel Grand Keisha Yogyakarta juga mempunyai staff Waiter dan Waiterss yang profesional serta selalu melakukan tugas dan tanggung jawabnya sesuai dengan Standard Operating Procedure (SOP).