bluuebunny/crossref_metadata_2025 huggingface dataset:
Sunlight to Sustainability: A Comprehensive Analysis of Solar Energy's Environmental Impact and Potential
Authors: Vadiyala Vishal Reddy
Published: January 2024

The study navigates the intricate landscape of solar energy, examining its historical foundations, environmental implications, economic viability, and transformative innovations. The study begins by elucidating the fundamentals of solar energy, exploring the principles of photovoltaic and solar thermal technologies. Subsequent chapters delve into the environmental footprint of photovoltaic technologies, revealing life cycle assessments and considerations for sustainability. The economic dynamics of solar energy are scrutinized, assessing market forces, government policies, and financial metrics. Innovation takes center stage in the final chapter, exploring advancements shaping the solar landscape. Major findings underscore the promising trajectory of solar energy, positioning it as a dynamic force in the global pursuit of sustainability. The study concludes by emphasizing the need for ongoing research, technological innovation, and strategic planning to fully unlock solar energy's potential in the transition towards a sustainable future.
Advanced Java Wizardry: Delving into Cutting-Edge Concepts for Scalable and Secure Coding
Authors: Fadziso Takudzwa , Vadiyala Vishal Reddy , Baddam Parikshith Reddy
Published: January 2024

The dynamic landscape of advanced Java is investigated in this essay, focusing on the essential features and techniques that propel engineers into the future of software engineering. Mastering multithreading and concurrency for best performance, as well as maximizing the potential of Java, are all topics that will be covered. An exploration of more complex notions that take Java programming to new heights is presented in this article. Learn the intricacies of web development, microservices, and secure coding techniques. This will ensure that readers understand the tools and methodologies driving the cutting edge of Java programming. Take advantage of insights designed explicitly for developers negotiating the difficulties of advanced Java and embrace innovation and scalability. This study provides developers with the information and skills to construct robust and high-performing applications. It covers subjects such as microservices architecture, reactive programming, and security best practices, among other topics. The purpose of this article is to provide a comprehensive investigation of advanced concepts that are necessary for the development of modern software.
Orthodontic bracket bonding techniques and adhesion failures
Authors: Almoabady Ebraheem Hamed , Alkahtani Reem Mohammed , Alotaibi Noura Fahad , Alsuli Mona Abdulmohsen , Alonazi Amera Saud , Alanazi Badriah Ayed , Madani Sara Ahmed , Ghadeer Sara Abdulaziz Bin , Alabbad Hawraa Mohammad , Wahbi Lulwa Ali Al , Alqahtani Mohammed Abdullah
Published: January 2024

The introduction of glass ionomer cements in orthodontics aimed to address the drawbacks of the acid-etch technique using composite resins, including demineralization, enamel damage, and potential allergic reactions. These cements release fluoride over time, potentially protecting against demineralization. Glass ionomer cements, despite their benefits in reducing adhesive residue and protecting against demineralization, showed higher bond failure rates compared to composite resins. This is attributed to their sensitivity to application techniques and moisture, along with a delayed setting time. In contrast, light-cured composite resins, preferred over chemically-cured resins in recent years, offer advantages such as ease of use, consistent handling, and controlled setting. However, early trials indicated higher bond failure rates for light-cured resins, a finding not consistently replicated in later studies. While glass ionomer cements offer certain advantages, their higher bond failure rate poses a significant limitation. Light-cured composite resins, with their user-friendly characteristics, have become the preferred choice in orthodontic bonding despite initial concerns about higher bond failure rates.
Long-term maintenance and complications of fixed dental prostheses
Authors: Binhuraib Haitham Mohammed , Alqarni Sundus Saad , Mohsen Nagam Mohammed , Alharbi Abeer Ahmad , Alghamdi Ahmed Ibrahim , Almotairi Adeeb Maseer , Alhussain Abdulrahman Hussain , Felemban Rahaf Ayman , Alzahrani Anwar Salem , Eyouni Ehdaa Sameer , Alhajjaji Razan Ibrahim
Published: January 2024

Fixed dental prostheses (FDPs), commonly known as dental bridges, play a pivotal role in restorative dentistry, offering patients a means to regain oral function and aesthetics in cases of tooth loss. These prosthetic devices, anchored to natural teeth or dental implants, bridge the gap created by missing teeth, improving chewing, speech, and smile aesthetics. However, the successful deployment of FDPs requires not only their initial placement but also long-term maintenance and an understanding of potential complications. Effective oral hygiene, regular dental surveillance, and professional dental cleanings are essential for preserving FDP longevity. Vigilance is crucial as FDPs, while resistant to cavities themselves, can pose challenges in cleaning around abutment teeth. Complications, including dental caries, periodontal issues, crown or bridge fractures, abutment tooth deterioration, aesthetic alterations, sensory sensitivity, and even prosthetic failure, are potential concerns. Timely intervention and preventive measures are key to addressing these issues. When complications arise, dental practitioners can often repair superficial problems like crown fractures. Profound or recurrent complications may require more extensive interventions, such as fabricating a new bridge or considering alternative restorative options like dental implants or removable partial dentures. By embracing these principles of maintenance and management, patients can optimize the durability and functionality of their FDPs while minimizing associated risks, ensuring a healthy and confident smile for years to come.
Economic losses due to abscesses and/or vaccine granulomas in carcasses of cattle slaughtered in Roraima state, Brazil
Authors: Ferreira Lima Everton , Compagnon Mariano Joicy , Alencar Conceição de Sousa Edson , Buzutti de Siqueira André , Pinto de Godoy Siqueira Heloisa
Published: January 2024

Beef production has been growing in Roraima state, Brazil, in recent years. As a result, poor sanitary management and final product quality have caused economic losses due to inappropriate vaccine use. Thus, the present study aimed at evaluating the economic losses caused by vaccine reactions due to abscesses and/or granulomas in cattle carcasses from slaughterhouses under veterinary inspection in Roraima. The study was carried out from August 2017 to July 2018, in a slaughterhouse, evaluating cattle carcass injuries. A total of 277 carcasses with vaccine reactions were identified, 57.03% with granulomatous lesions and 42.97% abscessive lesions, representing an economic loss of R$ 2,105.20. In regard to lesion location, there was a predominance on the side of the neck (91.34%). There was a statistical difference between the type of injury and carcass weight (P<0.05). The municipalities of Cantá, Caracaraí, Iracema and Rorainópolis showed a statistical difference in granuloma frequency for abscesses, demonstrating probable adequate vaccination management. The information obtained can be used to alert sanitary defense and public health services, through disease control programs for the state’s cattle herd. It is expected that these programs could improve final product quality, thereby reducing the economic losses of producers.
Classic and alternative sources of capital in the small and medium-sized enterprise sector in Polish conditions
Authors: Walczak Marcin
Published: January 2024

Purpose of the study: The main purpose of this article is to present selected classic and alternative sources of financing for small and medium-sized enterprises in Polish conditions. The article also aims to present the advantages and disadvantages of each source of capital.

Methodology: The article was prepared using the method of a critical review of the literature on the formation of the capital structure of enterprises and the choice of sources of financing the activities of the SME sector. The literature review conducted analyzed articles and other publications related to the topic.

Main findings: The presentation of selected sources of business financing, along with the advantages and disadvantages of using them, shows the multiplicity of choices available to managers, but at the same time warns against their improper use.

Application of the study: The analysis presented here makes it possible to systematize knowledge in the area of theory related to classical and alternative sources of business financing. The theoretical content is expanded to include an objective presentation of the advantages and disadvantages of using different ways of financing business activities. The research can help you decide on the appropriate source of business financing, mainly at the initial stage.

Originality/Novelty of the study: The study undertaken reveals space for further analysis of available sources of business financing, especially for large and advanced stage enterprises.
Learning Dayak Literature through Information Systems
Authors: Widiyarto Sigit , Sunendar Dadang , Vernia Dellia Mila , Alifah Siti , Suprapto Hugo Aries , Leksono Ari Wahyu , Rizkiyah Nur
Published: January 2024

This study aims to analyze old poetry (mantras) as part of learning literature during the Corona pandemic that is currently hitting Indonesia. Also, this study revitalizes culture by creating an information system application. This study used descriptive qualitative. There were 16 participants, consisting of the village community of Ranyai, cultures, traditional stakeholders, and village heads. This method provided an overview and described the research results on literary culture and an analysis of the structure of prayer/mantra in the Gawai Dayak tradition and its revitalization efforts. The results showed that the structure of the text in the form of mantra utterances was based on the analysis of syntax, rhyme, and rhythm. The context of the Gawai Dayak tradition consists of cultural, situational, social, and ideological contexts. The context of the Gawai Dayak tradition consisted of motion, proxemic, paralinguistic, and material contexts. The design of an information system on Dayak literature could help realize the revitalization of cultural traditions and teaching and learning processes in a directed, effective, and consistent manner during the Corona pandemic. The implication for further research was the opening of further research developments, such as looking for values from tradition. Learning that used information systems was also still wide open to be developed with other more complete applications and under the times.
Spatiotemporal Analysis for Rainfall Prediction Using Extreme Learning Machine Cluster
Authors: Fredyan Renaldy , Majiid Muhammad Rizki Nur , Kusuma Gede Putra
Published: January 2024

Rainfall prediction is an essential study as a guideline for water resources management to manage disasters. Still, earlier research cares much about temporal information, only considering a single spatial location. The earthâ€™s land surface has a large area of spatial location, so to manage spatial information simultaneously as temporal, we use spatiotemporal data to analyze rainfall prediction more accurately. This study uses the spatiotemporal Extreme Learning Machines (ELM) Cluster to forecast rainfall using CHIPRS data from satellites and stations. Data consists of spatial two dimensions and temporal data from 1981 to 2020. The dataset for the experiment contains 480 months. We use focal operation for data preprocessing to the nearest neighbor value. Moreover, the ELM cluster can manage every spatial location by sharing the output weight of ELM, so there is no spatial information left behind. Then, comparing the spatiotemporal Extreme Learning Machines Cluster among SVR, Linear Regression, Gaussian, Ridge, and Lasso are used to predict the data on those timescales. The results indicate that spatiotemporal ELM-Cluster can accurately forecast rainfall. Using ELM-Cluster in hydrological rainfall forecasting is encouraging, and the model can practically be used. Evaluation using MAE with a score of 66.77 and RMSE, 83.77, getting the fastest training with only 28.9 seconds compared to the other methods due to the ELM Cluster does not have backpropagation with spatial improvement.
Analysis of Propagation Characteristics in Unmanned Aerial Vehicle (UAV) System
Authors: Wardihani Eni Dwi , Sari Tiara Nira , Setyawan Thomas Agung , Astuti Hany Windri
Published: January 2024

Unmanned Aerial Vehicle (UAV) has gained great attention to the spread of communication in civilian and military applications. UAV communication channel has its own characteristics compared to cellular and satellite systems, which are widely used. Thus, an accurate channel characterization is very important to optimize the performance and design of an efficient UAV communication system. However, several challenges exist in UAV channel modeling. For example, channel propagation characteristics of UAVs are still less explored. Therefore, this research discusses the propagation characteristics of UAV communication systems. Due to the limitation of the measurement tools, the propagation characteristics identified in this research was the pathloss coefficient value and optimum height based on the value of Received Signal Strength Indicator (RSSI) measurement results at different distance and heights. The link communication used 433 MHz telemetry. The results of pathloss coefficient at heights of 10 m, 20 m, and 30 m are 1.56 m, 1.77 m, and 1.99 m. While the results of the optimum height of 10 m, 20 m, and 30 m are 1.39 m, 1.32 m, and 1.47 m.
Fine Tuned of DenseNET121 to Classify NTT Weaving Motifs on Mobile Application
Authors: Maur Yohanes Eudes Hugo , Santoso Albertus Joko , Pranowo -
Published: January 2024

The problem of classifying Woven Fabric Motifs through pattern recognition can be addressed using Convolutional Neural Networks (CNNs). Existing CNN architectures like VGG, ResNet, MobileNet, and DenseNet offer diverse propagation methods. These architectures, trained on datasets like imagenet, have demonstrated competence in solving large-scale classification tasks. The CNN model trained on the ImageNet dataset, hereinafter referred to as the pre-trained model, can be utilized to address the classification issue of NTT woven fabric motifs. This involves retraining the model using a new output layer and dataset, a method known as Transfer Learning. In addition to Transfer Learning, this research employs Fine Tuning, which entails retraining several classification layers. The pre-trained model used in this research is DenseNet121. This model was chosen because it does not require too much storage space and has good classification performance so that it can be embedded in smartphones. The results of this study indicate that of the three pre-trained models tested (DenseNet121, MobileNetV2, and ResNet50V2), the pre-trained Model DenseNet121 is the model that has the highest accuracy and the smallest loss, namely 92.58% accuracy and 29.62% Loss. Tests on mobile devices also show that from 130 test data, this model gets an accuracy of 99.23%. Overall, the classification model of NTT woven fabric motifs embedded in mobile devices can be used as an alternative to help the community or people who want to learn about NTT woven fabric motifs.
Optimization and Analysis of Polyhydroxyalkanoate (PHA) by Bacillus sp. Strain CL33 and Bacillus flexus Strain S5a from Palm Oil Mill Waste
Authors: Haedar Nur , Jamaluddin Mutia Putri , Fahruddin - , Dwyana Zaraswati , Zainuddin Zarlina , Gani Fuad , Tuwo Mustika
Published: January 2024

Polyhydroxyalkanoate (PHA) is a biodegradable polymer that microorganisms can synthesize amidst non-optimal growth conditions with excess carbon sources. Palm oil, rich in fatty acids, serves as a carbon source for PHA synthesis. The bacterial PHA production can be influenced by carbon concentration in the growth medium. Therefore, determining the optimal concentration of palm oil as a carbon source is crucial for PHA production. Additionally, it is possible to determine the type of PHA generated by bacteria, which can then be utilized as information when processing utilizing the PHA. The experiment employed palm oil concentrations of 0.5%, 1%, and 2% and was carried out for periods of 48, 72, 96 hours. It was discovered that Bacillus sp. strain CL33 and Bacillus flexus strain S5a produced the most effective PHA at a concentration of 25 with an incubation period of 96 hours. The PHA generated by these bacteria was quantitatively analyzed through measurements of total bacterial growth, cell dry weight, and the levels of crotonic acid. PHA types were also analyzed using GC-MS, with monomers including 2-hydroxybutyrate(-2HB), 2-hydroxy-3-phenylpropionate (2H3PhP), 3-Hydroxyhexanoate (3HHx), 3-hydroxyoctanoate (3H2O), and 3-hydroxydecanoate (3HD). The Bacillus sp. strain CL33 yielded a PHA level of 92.23%. Meanwhile, Bacillus flexus strain S5a synthesized a polyhydroxyalkanoate comprising mostly 3-hydroxyhexanoate (3HHx) and polydimethylsiloxane (PDMS). The monomers used were decamethyltetrasiloxane, dodecamethylpentasiloxane, hexamethylcyclotrisiloxane, octamethylpentasiloxane, and dodecamethylcyclohexasiloxane. The type of PHA produced accounted for 85.93% of the total.
The Impact of Online Transportation Services on Indonesian Urban Non-Working Trip Volume and Distribution Pattern: A Case Study in Bandung City
Authors: Miharja Miming , Desiana Renny , Kipuw Desiree Marlyn
Published: January 2024

The development of digital technology has made it much easier for most people to access online communication through smartphones. Such a phenomenon has encouraged operators to develop an online transportation service system, which has also been growing in Indonesian urban areas. The urban community accepts the new alternative of the application-based transportation system since it offers a much more efficient way to reach transportation services. Moreover, the online transportation system also offers a much wider range of destination locations. It is believed that the creation of an application-based transportation service system will affect metropolitan areas' social and economic conditions and how urban communitiesâ€™ travel. The development of this application-based transportation service would change the configuration of urban non-working trip distribution patterns, which should be understood as inputs for the urban transport development plan. This research aims to understand the impact of online transportation services on non-working trip distribution patterns in Indonesian urban areas, with Bandung City as the case study. The method used in this research is statistical descriptive, using respondents' responses to map the influenced trip generation and distribution patterns. The results show that online transportation has enabled the community to travel more easily and reach wider areas. Such changes have formed a new travel pattern that extends the destination locations to a wider peri-urban area. In the future, the government should anticipate urban fringe area land use growth, changing the region into more "urbanized-built-up" areas.
Benefits of Using Technology through the Use of Applications in Integrated Referral Services in Social Welfare Centers (Puskesos)
Authors: Kardeti Denti , Pribowo - , Rusmana Aep , Marjuki - , Rustanto Bambang , Kirani Ayu Mirah , Alfrojems -
Published: January 2024

Integrated social services organized through the SLRT (Integrated Referral Service System) by the Puskesos (Social Welfare Center) were a step forward in answering today's increasingly complex social problems. On the other hand, the development of technology and information provided opportunities and challenges to unite the two technologies and integrate social services into a single unit to strengthen further efforts to solve social problems. The involvement of technology through applications was a step forward and quite visionary to take advantage of technological developments, especially the closer technology and information to today's society. Based on these conditions, the purpose of this study was to examine deeply the benefits of using technology in integrated referral services carried out by Puskesos. In line with these objectives, the method used was qualitative with descriptive type. Data collection techniques include documentation studies, observations, in-depth interviews, and focus group discussions. Furthermore, the sampling technique in this study was purposive sampling, and the number of informants in this study amounted to 70, spread over two locations, namely Sleman Regency and Bandung Regency. The results show four benefits that were quite dominant in the use of technology in integrated social services: effectiveness, efficiency, accountability, and public trust. Realizing this benefit, it is hoped that other supporting programs will be involved in the form of socialization related to the use of technology through the use of applications in these services.
Ambiguity Detection and Improvement for Malay Requirements Specification: A Systematic Review
Authors: Zahrin Mohd Firdaus , Osman Mohd Hafeez , Hassan Sa'adah , Haron Azlena , Abdul Halin Alfian
Published: January 2024

Malaysian public sectors have invested billions in digitizing systems. Electronic government efforts created much software. Our informal interview taught us that many software projects encountered delays, and several failed. One of the main contributions of software failure is ambiguity in requirements specification (RS). Ambiguity is a familiar requirement smell that causes misinterpretation. Thus, we seek to devise a technique for detecting and improving ambiguous RS in the Malaysian public sector. One of our challenges is that the Malaysian public sector RS is developed in Malay, and most available techniques support English and other major languages. Hence, this paper investigates the automated and semi-automated techniques to detect and improve ambiguous RS. Following the standard guidelines for systematic mapping, review, snowballing, and quality assessment, we studied works from 2010 to 2022 on ambiguity detection and improvement techniques. We chose 42 articles as primary studies from 2,549. As a result, Natural Language Processing (NLP) and machine learning (ML) are the most promising techniques for automated and semi-automated ambiguous detection models. Furthermore, the ambiguous improvement technique began using deep learning (DL) in 2019. However, most proposed tools are still in the validation phase and are not widely employed, implying that tool development and validation research are progressing slowly. Apart from the generic linguistic context of RS, some research focuses on industrial domain-based RS. Our study shows that additional strategies have been developed to overcome RS-related issues.
Assessment of Maintenance Performance Using the Maintenance Scorecard Method and Prioritization of Problem Control Strategies with the USG Method
Authors: Nurcahyo Rahmat , Maulida Duhita Wahyu , Susanto Danar Agus , Kristiningrum Ellia
Published: January 2024

Measuring the performance of machine maintenance becomes very important, serves as a monitoring tool, and triggers increased performance in the production section. Losses due to engine damage will impact the company's profit, which is less than optimal because maintenance can contribute as much as 20-50% of the cost composition of the company's operational costs. This study aimed to measure performance in the maintenance section using the maintenance scorecard (MS) and determine the priority of the performance control strategy using the Urgency, Seriousness, and Growth Method (USG). The research was conducted through case studies on chemical manufacturing companies located in Indonesia. The performance assessment results with the maintenance scorecard method show that the total scorecard maintenance value is in the category of need improvement, which is 63.35. There are 3 KPIs in the very bad category: maintenance costs from a cost-efficiency perspective, work completed from a quality perspective, and self-audit from a learning perspective. The first and foremost strategy that can be done based on USG's priority is to implement reliability-centered maintenance (RCM) to reduce time loss and increase the knowledge and competence of employees. This priority is useful when a company encounters a constraint so that it cannot carry out all strategies simultaneously or can only carry out some strategies while still getting optimal benefits from continuous improvement.
Regression-based Analytical Approach for Speech Emotion Prediction based on Multivariate Additive Regression Spline (MARS)
Authors: Triandi Budi , Efendi Syahril , Mawengkang Herman , Sawaluddin -
Published: January 2024

Using regression analysis techniques for speech-emotion recognition (SER) is an excellent method of resource efficiency. The labeled speech emotion data has high emotional complexity and ambiguity, making this research difficult. The maximum average difference is used to consider the marginal agreement between the source and target domains without focusing on the distribution of the previous classes in the two domains. To address this issue, we propose emotion recognition in speech using a regression analysis technique based on local domain adaptation. The results of this study show that the model's generalization ability with the function of the local additive method is very good for improving speech emotion recognition performance. Even though it provides excellent benefits in resource efficiency, regression analytical techniques are rarely used in the SER field; however, we believe this method is the best solution for SER problems. Using the Multivariate Additive Regression Spline, this study developed a predictive model for the existence of angry and non-angry emotions (MARS). Using probability analysis of error values, this approach can overcome regression on data that is not typically distributed. This method yields an ideal basis function that significantly impacts changes in emotional form. This study generates a prediction model with a Mean Square Error (MSE) of 0.0130, a Generalized Cross Validation (GCV) value of 0.0062, and a R Square (RSQ) value of 0.9721, yielding test results with a 97% accuracy rate.
Development of Mobile Learning Based on Digital Entrepreneurs Using Raspberry Pi on TVET
Authors: Fadillah Rahmat , Ganefri - , Yulastri Asmar , Hidayat Hendra
Published: January 2024

Mobile learning is a method of instruction that uses portable electronics like laptops, smartphones, mobile phones, and PCs so that students may access lesson materials, instructions, and apps whenever and wherever they are without being constrained by time or place. This is an innovation regarding a systematic and structured application-based learning system as an interactive medium for students, especially at TVET. This article aims to develop mobile learning using a mini server Raspberry Pi 3 Model B+ based on digital entrepreneurs (digipreneur) that runs on the Moodle LMS as a source of learning content so that learning can be carried out interactively and flexibly, without having to be connected to the internet, or classrooms that are not effective even in areas with no internet access. This activity goes through 2 stages of system design known as client-server. In designing the server, the Raspberry Pi mini server model B+ configuration is carried out as a source of digital learning resources by using several supporting applications such as MySQL server, SSH, PHPMyAdmin, and Apache in designing the client using the Moodle LMS application, which contains digipreneur-based digital learning materials and resources, all stored in the database server. Three primary users are built into the Mobile Learning Database System: Administrators, Lecturers, and Students. This application is expected to be innovative and the right solution in terms of learning and become an alternative problem-solving tool in education.
Measurement Analysis of Non-Invasive Blood Glucose On Sensor Coplanar Waveguide Loaded Square Ring Resonator with Interdigital Coupling Capacitor
Authors: Ratmoko Catur , Laisa Iryanto , Alaydrus Mudrik
Published: January 2024

This study presents the experimental results of a system with a sensor structure detecting human blood glucose levels. A microwave-based sensor is used for non-invasive blood glucose monitoring. The sensor design uses an asymmetrically loaded CPW structure as a square ring resonator with an interdigital coupling capacitor on the ground side. Simulated with a load of artificial finger tissue made from gelatin, modeled in four layers. The first layer is the skin is the outermost tissue, the next layer is fat, blood and bone. Each layer of tissue has a certain thickness size; skin (0.3mm), fat (0.2mm), blood (1.5mm), and bone (4mm). The measurement simulation is used, HFSS as modeling simulation and VNA as a measurement of the physical representation of the design results with parametric optimization methods. To verify the correlation and the expected sensitivity, media with different dielectrics were mounted on the surface of the sensor resonator with blood glucose levels of 1mg/dl, 72mg/dl, 126mg/dl, 162mg/dl and 216mg/dl. Reflection factor S11 was observed based on dielectric constant blood glucose levels (dB) fluctuations. Analysis of the data on the graph between the independent variables, namely blood glucose concentration and the dependent variable levels of S11 has an â€œRâ€ correlation value of 0.97. The sensitivity level of the sensor on the S11 reflection factor with HFSS simulation averages 73.36mdB/mgdl-1 and VNA reaches 82.39mdB/mgdl-1. The results are interesting for developing a more optimal glucose sensor system.
Multitemporal Acoustic Backscatter Data Analysis to Monitor the Dynamics of Seabed Surface Sediments
Authors: Pratomo Danar Guruh , Khomsin - , Hariyanto Irena Hana , Putri Nabilla Aprilia , Mudita Imam , Haryanto Dwi , Safiâ€™ Ahmad Fawaiz
Published: January 2024

Backscatter strength is a product of underwater acoustic remote sensing. This study used a multibeam echosounder as an acoustic remote sensing instrument to collect backscatter strength data. These data are then used to classify the surficial sediment distribution. To monitor the difference in seabed sediment distribution, a time-series survey was performed to obtain multitemporal acoustic backscatter data. An EM 304 multibeam system from Kongsberg was mounted on the Research Vessel Baruna Jaya III from the Indonesia National Research and Innovation Agency (BRIN). It was used to collect backscatter data in the waters of Raja Ampat, Indonesia. The data were collected at different times, April and July 2021. Geometric and radiometric corrections were applied to these backscatter data. Based on the angular response curve analysis from acoustic backscatter strength, the research area can be classified into four seabed sediment types: boulder, gravel, sand, and mud. A comparison of both time series backscatter data shows that the boulder and gravel areas increased by 13.6% and 19.0%, respectively. Elsewhere, the area with sediment types of sand and mud diminished by 30.5% and 2.0%. The change in the sediment type area occurred as much as 50.5%, while the remaining 49.5% area remained unchanged. This resulting value is apparently derived from the steep topography that rapidly changes sediment distribution. One such suggestion is that sediment sampling should be performed to confirm the model from angular response curve analysis.
An Extreme Programming Approach to Streamlining Thesis Writing
Authors: Semma Andi Bahtiar , Saerozi Muh , Kusrini Kusrini , Syukur Abdul , Maimun Achmad
Published: January 2024

Thesis writing is a significant challenge for many university students, often leading to substantial stress, confusion, and distress. Despite numerous studies to reduce academic stress, a specific solution for thesis writing has yet to be identified. This study introduces Penamasy, a thesis writing management system designed to address this issue. Penamasy was developed using the Extreme Programming (XP) methodology, effectively addressing the rapidly changing demands of university students in an academic environment. The result was a user-friendly app delivered promptly and met the needs of students. The study consisted of three research cycles, including user requirements, release planning, and acceptance tests, followed by an extended user acceptance test with students and promoters from three universities. Results indicated a positive response, with 79.6% of respondents expecting a more systematic and fuller online thesis writing experience, enabling them to complete their theses promptly. In conclusion, Penamasy provides a solution for university students facing the stress and difficulties of thesis writing. By streamlining the process and offering a systematic approach, students can focus on their research with confidence in the control of their writing process. Nevertheless, this study can be used as a guide in resolving students' academic stress and many other pragmatic problems that occur, especially in an educational environment. Future studies should involve users choosing UI component libraries, performance evaluation, and possible workflows.
Analysis and Evaluation of PointNet for Indoor Office Point Cloud Semantic Segmentation
Authors: Wijaya Calvin , Harintaka -
Published: January 2024

Indoor modeling is one of the primary sources of information in building management due to the increased use of BIM in the AEC industry. The indoor model can be acquired with several survey instruments, but TLS is the most popular resulting point cloud that can be processed into a 3D model. However, the process commonly still uses inefficient manual methods. Point cloud data have irregular, unordered, unstructured characteristics, making them more challenging to process. The deep learning algorithm can be a solution to solve the problem. PointNet is the first deep learning algorithm that directly accepts point cloud data as input. This study aims to analyze and evaluate the office indoor point cloud segmentation using PointNet. The office indoor point cloud data was acquired using TLS and then pre-processed for deep learning input. Transfer learning strategy is used as a weight initialization technique. The pre-trained model was trained with the S3DIS dataset and then fine-tuned to segment nine indoor classes in this study. The result shows PointNet achieves 85% overall accuracy and 66% average class IoU score to predict indoor classes using this studyâ€™s point cloud data. Geometry control shows that the predicted point cloud has an RMSE score of 1.8 cm, meaning the geometries of the segmented point cloud are accurate. Using the transfer learning method has increased the performance of the deep learning model. Further research is needed to evaluate the model thoroughly using more training and evaluation data and different transfer learning strategies.
Effect of Calcination on the Ash from Lokon Volcano and Its Potentially Sustainable Binder Material
Authors: Bobanto Maria Daurina , Pandara Dolfie Paulus , Ferdy Ferdy , Tiwow Vistarani Arini , Aritonang Henry , Tanauma Adey , Todingan Rezkyana
Published: January 2024

The need for cement as a housing construction material has continued to increase due to the growing population. This high demand increases carbon dioxide emissions. Hence, it is necessary to optimize the use of natural pozzolan material. Volcanic ash is a natural pozzolan material in North Sulawesi, but its use could be more optimal. This study aimed to determine the effect of calcination on the physical properties of volcanic ash originating from the eruption of Mount Lokon. The calcination was carried out to determine the potential of Lokon ash at different temperatures to assess the structural characteristics, mineral phases, metal oxide composition, functional group bonding, morphology, and its potential as a binder for concrete mixtures. The ash material used comes from sand taken from the Pasahapen River and filtered through a 325-mesh sieve. Lokon ash was calcined at temperatures of 800, 900, and 1000oC to determine the structural and morphological characteristics. At the same time, the effects were examined using an X-ray diffractometer (XRD), Raman spectroscopy, X-ray fluorescence (XRF), Fourier Transform InfraRed (FTIR), and Scanning Electron Microscopy (SEM). The results showed that calcination triggered the formation of hematite in the ash, which will increase its reactivity as a pozzolan material. This process causes the crystallinity of ash minerals to increase, but the ash material produced is predominantly amorphous. Hence, it has excellent potential as a binder material in concrete mixtures.
Analysis of Dam Break Wave Using Analytical, Computational Fluid Dynamics, and Experimental Approaches
Authors: Noviani Evi , Putra Yoga Satria , Suheri Cucu
Published: January 2024

This research aims to examine the capability of the Computational Fluid Dynamics (CFD) method in simulating the behavior of dam break waves. It begins by building a 2D numerical simulation using OpenFOAM. To overcome the influence of turbulence, we employed the Large Eddy Simulation (LES) turbulent model, specifically the k-Equation and Smagorinsky model. The simulation was developed by applying the Navier-Stokes equations using the finite volume method in OpenFOAM. The analysis focuses on the free surface of a dam break. The results are in good accordance with both analytical and experimental results. The simulation has followed the trend of experimental and analytical free surface profiles at the dam breakâ€™s early and late conditions. The low mesh number on the computational domain caused significant differences in the wavefront of the dam break. It reduced the accuracy of the calculation between the water and air interface. This study highlights the importance of understanding dam break wave behavior as part of risk mitigation for dam leakage. The behavior of dam break waves can be observed by determining observation positions at different locations, with the water gate of a dam serving as the reference point. These highly accurate numerical results indicate that the CFD approach employing OpenFOAM can be relatively cost-effective yet accurate in analyzing multiphase problems, such as dam breaks. This CFD approach is expected to contribute to developing mitigation and disaster prevention in the future.
Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
Authors: Chaoraingern Jutarut , Tipsuwanporn Vittaya , Numsomran Arjin
Published: January 2024

BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.
Telecommunication Fiber Box Detection Using YOLO in Urban Environment
Authors: Azmawi Azib-Jazman , Mohd-Isa Wan-Noorshahida , Rahman Abdul Aziz Abdul
Published: January 2024

The Fiber Distribution Panel (FDP) box is an essential piece of internet access hardware because it provides users with high-speed data networking and functions as a cable organizer to reduce wire clutter. After installing the FDP, an inspection must be performed to ensure that all necessary components are present. However, This examination is still done manually; the technician snaps a picture of the panel and sends it to its supervisor for verification, which is time-consuming and often prone to errors. In addition to images captured in low-light and complex environments, it makes it more difficult for humans to identify the components with just a naked eye. On this matter, a much more efficient method to assess the FDP installation work is very much needed. Therefore, using computer vision approaches, we utilize a deep learning algorithm to perform object detection and automate the assessment of FDP installation components based on visual data. One of the deep learning models established in the literature is the You Only Look Once (YOLO) model, a one-stage deep learning object detection algorithm that employs a fully conventional approach to generate highly accurate real-time predictions. This paper uses YOLOv5s to identify the fiber box and its relevant components, even in urban environments. Experimentations show that YOLO successfully identified the installation parts with a mean average precision score of 86% at a 0.5 confidence level, even with limited data.
Utilization of Sargassum Flour Fermented with Bacillus aerius Bacteria in Siganus guttatus Rabbitfish Enlargement Feed
Authors: Kamaruddin - , Haryati - , Aslamyah Siti , Karim Muhammad Yusri
Published: January 2024

This Sargassum is a staple food of rabbitfish in nature; although its nutritional quality is relatively low if it is used as one of the feed ingredients first carried out in the fermentation process, the results of this fermentation are used as one of the raw feed materials. The study was conducted to look at the use of fermented sargassum flour in feed at the lowest dose of 7.5% to the highest dose of 30%. This study was designed using a completely randomized design (RAL). Using 15 units of floating net cage containers. The study duration was 120 days. During the study, the test fish were fed thrice daily, namely 08.00 am, 1:00 pm, and 03:00 pm. Feeding is carried out. To determine the severity of the treatment of the biological response of test fish, sampling is carried out every 30 days. From several parameters observed, such as weight gain, specific growth rate, and daily feed consumption rate, although statistically not different (P>0.05), the tendency to increase with the increase in sargassum dose by 30%. Similarly, the total digestibility of feed, digestibility of feed protein, and digestibility of feed energy statistically do not show a difference (P>0.05) but also the tendency to increase. Based on the results of this activity, it was concluded that sargassum flour that has been fermented with bacillus aerius bacteria can be used well by rabbitfish, with as much as 30% in the feed.
Performance Analysis of New 2D Spatial OCDMA Encoding based on HG Modes in Multicore Fiber
Authors: Sahraoui Walid , Amphawan Angela , Jasser Muhammed Basheer , Neo Tse-Kian
Published: January 2024

This paper presents a pioneering 2D spatial Optical Code-Division Multiple Access (OCDMA) encoding system that exploits Mode Division Multiplexing (MDM) and Multicore Fiber (MCF) technologies. This innovative approach utilizes two spatial dimensions to enhance the performance and security of OCDMA systems. In the first dimension, we employ Hermite-Gaussian modes (HG00, HG01, HG11) to modulate each user's signal individually. This unique approach offers a robust means of data transmission while ensuring minimal interference among users. The second-dimension leverages MCF encoding, introducing two incoherent OCDMA codes: the Zero Cross Correlation (ZCC) code (Î»c=0) and the ZFD code (Î»c=1). These codes are thoughtfully designed and simulated, taking into account their cross-correlation properties to guarantee minimal interference and heightened data security. To assess the efficiency of this novel OCDMA encoding system, we implemented simulations with three active users using the Opti system software. At the transmitter end, each user's signal is modulated individually by their designated HG mode (HG00, HG01, HG11), resulting in separate channels. Subsequently, at the multicore fiber, each user's data is encoded with a unique code-word, and they are directed through specific core groups, ensuring data isolation and integrity. In this paper, the BER and eye pattern are examined with respect to different parameters such as data rate and distance. At a distance of 5 km and data rate of 10 Gbit/s, a BER value around 10-70 is achieved.
Facial Skin Type Analysis Using Few-shot Learning with Prototypical Networks
Authors: Yeo Quan Fong , Ooi Shih Yin , Pang Ying Han , Gan Ying Huey
Published: January 2024

Facial skin type analysis is a critical task in several fields, including dermatology, cosmetics, and biometrics, and has been the subject of significant research in recent years. Traditional facial skin type analysis approaches rely on large, labeled datasets, which can be time-consuming and costly to collect. This study proposes a novel few-shot learning (FSL) approach for facial skin type analysis that can accurately classify skin types with limited labeled data. A diverse dataset of facial images with varying skin tones and conditions was curated. The proposed approach leverages pre-trained deep neural networks and an FSL algorithm based on prototypical networks (PNs) and matching networks (MNs) to address the challenge of limited labeled data. Importantly, this study has significant implications for improving access to dermatological care, especially in underserved populations, as many individuals are unaware of their skin type, which can lead to ineffective or even harmful skincare practices. Our approach can help individuals quickly determine their skin type and develop a personalized skincare routine based on their unique skin characteristics. The results of our experiments demonstrate the effectiveness of the proposed approach. PNs achieved the highest accuracy in the 2-way, 10-shot, 15-query scenario with an accuracy of 95.78 Â± 2.79%, while MNs achieved the highest accuracy of 90.33 Â± 4.10% in the 2-way, 5-shot, 10-query scenario. In conclusion, this study highlights the potential of FSL and deep neural networks to overcome the limitations of traditional approaches to facial skin analysis, offering a promising avenue for future research in this field.