bluuebunny/crossref_metadata_2025 huggingface dataset:
Analisis Perbaikan Tidak Terpancarnya Suara Air Traffic Controller (ATC) Ke Pesawat Pada Peralatan Sistem Komunikasi Penerbangan VHF Merek PAE T6T
Authors: Okta Family I Made Okta Dwipayana , Wahyudi Johan , Azka Muizuddin
Published: November 2024

Peraturan Keselamatan Penerbangan Sipil Bagian 171 dalam Peraturan Menteri Perhubungan Nomor PM 38 Tahun 2014 menyatakan bahwa layanan komunikasi penerbangan mencakup siaran, penerbangan, dan informasi cuaca. VHF A/G adalah sarana komunikasi penerbangan yang digunakan untuk berkomunikasi antara pesawat dan petugas Air Traffic Controller (ATC). Dalam pelaksanaan On the Job Training (OJT) di LPPNPI Cabang Palangka Raya terdapat permasalahan yang dikemukakan yaitu peralatan VHF A/G tidak dapat mengirimkan voice petugas ATC ke pesawat karena modul relay dalam peralatan mengalami permasalahan.
Pada pengiriman suara ATC peralatan VHF A/G merek  PAE T6T yang tidak  dapat di dikirimkan ke pesawat udara, dilakukan pemeriksaan pada  back panel peralatan. Ditemukan bahwa jumper pada PCB relay terputus dan baut pada relay longgar.  Hal ini menyebabkan peralatan  VHF A/G tidak dapat mengirimkan suara ke pesawat. Perbaikan dan penyambungan jumper dilakukan pada modul relay. Setelah  penyolderan dan pengencangan baut pada modul relay, peralatan VHF A/G kembali  berfungsi, ditandai dengan tidak menyalanya lampu indikator alarm.
Penelitian ini menggunakan metode kualitatif dengan fokus pada studi kasus yang mencakup proses penelitian. Tujuannya adalah menganalisis langkah-langkah yang dilakukan dalam perbaikan peralatan VHF A/G agar perangkat tersebut dapat mengirimkan suara petugas ATC kepada pilot dengan baik.
A Review on AMRR and Improved Round Robin Algorithms: Comparative Study
Authors: Putra Tri Dharma , Purnomo Rakhmat
Published: November 2024

Round Robin Algorithm is a dominant algorithm in real time system. Improved round robin and average max round robin, which is also called AMRR are two types with a breakthrough. Improved round robin is an algorithm where if the remaining burst time of the process is less than the quantum, then the running process will continue to be executed. Afterwards the next iteration will be executed as its turn. So, each iteration will have a vary of quantum. It is called a dynamic time quantum. Different with improved round robin, in AMRR, in every iteration, the quantum will be calculated. So, for every iteration, the quantum might be different, depending upon the quantum calculation of the rest burst time. The first stage of this algorithm is to calculate the average of the existing burst times. Then this average is added with the maximum existing burst time. This addition then will be divided, then we get the quantum. This calculation will be executed again after the iteration finish. Based on our analysis, with quantum 10 in these two algorithms. It is can be shown that the improved round robin is less efficient than AMRR, because its average turnaround time and average waiting time is lower. The average turnaround time is 17.25 ms for AMRR compared to 23.25 ms in improved round robin. And the average turnaround time is 9 ms for AMRR compared to 15 ms in improved round robin.
Integrated MCDM-AHP and MABAC for Selection Head of Branch Offices
Authors: Akmaludin Akmaludin , Suriyanto Adhi Dharma , Iriadi Nandang , Widianto Kudiantoro
Published: November 2024

Leadership changes are very urgent in maintaining organizational stability. A good relay can build significant strength in carrying out organizational operational activities, of course this must be done with good selection. The purpose of this writing is to provide a consistent picture of the selection of branch heads in carrying out business competition which is measured based on the competencies possessed by the selected employees. The barometer is determined based on eight criteria as an assessment that is declared objective by the leadership, namely critical thinking, communication, analyzing, creative and innovation, leadership, adaptation, cooperation, and public speaking. The method used will be implemented in an integrated manner from the two MCDM-AHP methods and the MABAC method. These two methods have similar applications to the selection process. MCDM-AHP is used to select eight criteria as determinants of weighting and the MABAC method is used to determine the ranking process assessment for integrated decision making. The results obtained based on the weighted matrices of the branch head office selection process were measurably obtained, namely that the first priority was held by A11 with a weight of 1,406. The results of the integrity of both methods can provide evidence of decision support for the branch head selection process consistently with optimal results. The ranking system can be regulated and utilized for the purposes of selecting leaders to be placed in other positions.
Performance Analysis of AODV and DSDV Routing Protocols for UDP Communication in VANET
Authors: Bintoro Ketut Bayu Yogha , Marchenko Michael , Saputra Rofi Chandra , Syahputra Ade
Published: November 2024

In high-mobility Vehicular Ad hoc Networks (VANETs), maintaining a low Packet Loss Ratio and a high Packet Delivery Ratio (PDR) under UDP communication is crucial. This study compares the performance of Ad hoc On-Demand Distance Vector (AODV) and Destination-Sequenced Distance-Vector (DSDV) routing protocols in vehicular communications and networking using Network Simulator 3 (NS3) simulations. The research employs a simulation-based approach, leveraging NS3 and SUMO to analyze these protocols across different VANET scenarios, including free flow, steady flow, and traffic jams over varying time intervals (300 to 700 seconds). Our findings demonstrate that AODV outperforms DSDV. AODV maintained an average Packet Loss Ratio of 98% and achieved higher throughput, while DSDV experienced higher packet loss and lower throughput. Additionally, AODV exhibited lower end-to-end delay and a higher Packet Delivery Ratio compared to DSDV. These results indicate that AODV is better suited for UDP communication in VANETs, offering lower packet loss, higher throughput, and reduced delays. The study further emphasizes that AODV is preferable for UDP communication in VANETs due to its superior performance metrics. There is potential for further research in vehicular communications, such as integrating advanced hybrid routing protocols and exploring the effects of different traffic densities, vehicle types, and real-world environmental conditions. By investigating these factors, future studies can enhance the reliability and efficiency of VANET communications, contributing to the advancement of intelligent transportation systems.
Comprehensive Study of Information Technology Strategy Components in Global ICT Companies Utilizing PESTLE and Ansoff Matrix
Authors: Purawidjaja Ratna Amalia , Chudra Glenny , Yohannis Alfa
Published: November 2024

This research underscores the diversity and strategic significance of IT Strategy components in shaping the digital transformation and competitive edge of ICT companies. The formulation of IT Strategy documents is pivotal for industries, including ICT companies, as it ensures alignment with business goals and competitive positioning. This study conducts a comprehensive literature review of IT Strategy components within global ICT companies, specifically those specializing in telecommunication network infrastructure. Despite operating within the same sector, each company’s IT Strategy document comprises distinct components. The identified components include Auditor Report, Business Strategy, Leadership, Product/Service Line, Geographic Performance, Research & Development, Partnership & Acquisition, Summary Report, Corporate Governance, Vision & Mission, Financial Statement, Industry Trends, and Business Highlights.  These components are essential for aiding the organization’s IT Strategic Plan and the creation of the company's roadmap. Furthermore, this study identifies PESTLE analysis and the Ansoff Matrix as crucial tools in creating strategic roadmaps tailored to each company’s unique objectives and market conditions.
Model Random Forest and Support Vector Machine for Flood Classification in Indonesia
Authors: Purwati Sintia Eka , Pristyanto Yoga
Published: November 2024

People, especially those living in lowland areas and along rivers. This flood phenomenon significantly affects various aspects, both in terms of economics, environment, and public safety. Flooding is a disaster that often causes problems for most people, especially those living in lowland areas and on riverbanks. This flood phenomenon significantly affects various aspects, such as the economy, environment, and community safety. This research compares the Random Forest and Support Vector Machine (SVM) methods for flood classification in Jakarta. The data used is flood data from 2016 – 2020 in Jakarta, obtained from Kaggle. Model performance evaluation is carried out using accuracy, precision, recall, and F1- Score metrics. The analysis results show that both models accurately classification floods, with Random Forest showing a more stable performance than SVM.
Application of the Arima Method to Prediction Maximum Rainfall at Central Java Climatological Station
Authors: Ruslana Zauyik Nana , Prihatin Rudi Setyo , Sulistiyowati Sulistiyowati , Nugroho Kristiawan
Published: November 2024

The existence of extreme weather that is difficult to predict results in frequent hydrometeorological disasters. ARIMA is a prediction method that can capture trend patterns, seasonal cycles, and random fluctuations that are often found in patterned data. Although many samples of rain data collection points are needed to produce denser data, one point can be considered to represent an area that is not too large, such as Semarang City. This method is quite accurate for short-term forecasts, with the results of monthly maximum rainfall forecasts in 2023 showing varying MAPE values. For the 12-month forecast, prediction results range from fair to very accurate. The 7-month forecast also shows decent to very accurate results. However, the 5-month forecast shows less accurate results. This shows that ARIMA can be a useful method in forecasting monthly maximum rainfall, especially during the dry season. The application of ARIMA in Semarang City can help in planning hydrometeorological disaster mitigation, considering that the Semarang City area often experiences extreme weather that is difficult to predict. Thus, the use of ARIMA can provide significant benefits in preparing for and reducing the impact of hydrometeorological disasters in the region. In addition, with more accurate forecasts, the government and society can take preventative steps earlier, such as better water management, creating an adequate drainage system, and increasing public awareness of the threat of disasters. Therefore, this research emphasizes the importance of using reliable prediction methods such as ARIMA to improve preparedness in dealing with hydrometeorological disasters.
Performance Comparison of ARIMA, LSTM, and Prophet Methods in Sales Forecasting
Authors: Suryawan I Gede Totok , Putra I Kadek Nurcahyo , Meliana Putu Mita , Sudipa I Gede Iwan
Published: November 2024

The development of the business world that is growing rapidly today  resulted in tighter competitiveness between fellow business actors. One of the businesses that has sprung up in the market today is the bakery business. Currently, bread is one of the food needs in Indonesia that is  great demand by children to the elderly, which is often used as breakfast or snack. One of the companies that produces white bread is the Bandung White Bread Factory. The number of sales at this factory continues to increase every month based on total sales data recorded since 2021. With the increasing number of sales at this factory, the factory often experiences stock shortages and cannot meet customer demand. Therefore, in this study, a model has been developed to forecast the sales of white bread using the ARIMA, LSTM, and Prophet methods. The results of the study showed that the ARIMA method (1,0,2) had the best performance compared to the LSTM and Prophet methods, because the ARIMA method (1,0,2) produced the smallest error accuracy value, namely with a MAPE value of 4.548%, an MSE value of 2248.0822, and an RMSE value of 47.4139.
Performance Single Linkage and K-Medoids on Data with Outliers
Authors: Allo Caecilia Bintang Girik , B Winda Ade Fitriya , Paranoan Nicea Roona
Published: November 2024

One way to assess the economic growth of a province is by examining its Gross Regional Domestic Product (GRDP). GRDP calculated through the production approach reflects the total value added by goods and services from various sectors within a particular region over a specified period. To determine the GRDP, 17 business sectors are considered. In 2023, the GRDP growth rate in Papua has decreased to 3.44%, down from 4.11% the previous year. To help the government improve Papua’s GRDP, an analysis is required. Clustering methods can group regencies and cities with similar characteristics. Boxplots are used to identify outliers in the data. The data contains outliers, so one method that can be used is K-Medoids. Euclidean Distance is used to calculate the distance matrix. Before calculating the distances, standardization using z-score normalization is performed to ensure that the data ranges are the same. This article aims to identify the most effective method for clustering regencies and cities in Papua using GRDP at constant price data. Both Single Linkage and K-Medoids methods are applied in this study. The DBI is used for evaluation, with lower DBI values indicating better methods. According to the DBI results, Single Linkage outperforms K-Medoids for clustering regencies and cities in Papua, with the optimal number of clusters being three.
Keywords: Euclidean Distance; Davies Bouldin Index (DBI); Gross Regional Domestic Bruto; K-Medoids; Single Linkage; z-score Normalization
Implementation Docker and Kubernetes Scaling Using Horizontal Scaler Method for Wordpress Services
Authors: Suryayusra Suryayusra , Destarina Nova , Negara Edi Surya , Supratman Edi , Ulfa Maria
Published: November 2024

Container is a technology that has recently been widely used because of the additional features that are very easy and convenient to use, especially for web hosting service developers, with Container making it easier for system admins to manage applications including building, processing and running applications on Container. With Container the process of creating and using the system will be easier but along with too many user requests so that the service does not run optimally. Therefore, the Container must have good scalability and performance. Scalability is needed for systems that can adjust to the needs of user demand and performance is needed to maintain the quality of services provided. This research aims to implement scaling using Docker and Kubernetes in terms of scalability and performance. The parameters of comparison between Docker and Kubernetes are for scalability, scaling up and scaling down time and for performance. The method in this research uses the Action Research methodology, which is a research model that is simultaneously practiced and theorized. With the initial steps of problem identification, action planning, action implementation, observation and evaluation. Based on the results that have been obtained, Docker consumes more CPU & Memory Usage Resources, namely at 500 Users Kubernetes consumes Resources with an average of 94.47%-4.70% while in Kubernetes 89.11%-4.50 because in Kubernetes itself has a complex system, especially special component components such as APIs, Metrics Server, Kubernetes manager to run the Container. While in Docker only has Docker Manager and Docker Compose components.
Cluster Analysis of Food Social Assistance in DKI Jakarta: K-Means Approach to Identify Expenditure Patterns and Beneficiaries
Authors: Suharyanti Nining , Rusdiansyah Rusdiansyah , Supendar Hendra , Tuslaela Tuslaela
Published: November 2024

This study aims to evaluate the effectiveness of the K-Means algorithm in grouping social assistance recipients in DKI Jakarta based on various demographic and economic factors, such as income, number of family members, and living conditions. The main objective of this study is to optimize resource allocation in social assistance programs by identifying different recipient clusters, so that aid distribution becomes more targeted. In this study, the K-Means algorithm was used with an optimal number of clusters of 3, and produced an accuracy rate of 85%, indicating that this algorithm is effective in grouping large-scale and complex data. However, there are challenges related to the sensitivity of K-Means to outliers and data imbalances that affect the results of the analysis. The results also show that areas such as Central Jakarta and South Jakarta receive more social assistance compared to other areas such as North Jakarta and East Jakarta, reflecting differences in needs in various regions. These findings emphasize the importance of selecting the right variables, such as access to health facilities and economic conditions, in producing more accurate groupings. Overall, this study provides valuable insights into efforts to optimize the distribution of social assistance in DKI Jakarta and recommends further research to address the limitations that exist in the use of the K-Means algorithm, especially in the context of data that is imbalanced or has large variations.
Web-Based Application Development using PHP-Native Framework on Agent of Change Integrity Zone Information System
Authors: Perdana Adidtya , Farhana Nurul Ain , Harliana Putri , Karo Karo Ichwanul Muslim
Published: November 2024

An Integrity Zone is an activity related to government policies and efforts to create an environment free from corruption, collusion, and nepotism (KKN). The Integrity Zone aims to encourage transparency, accountability, and clean practices in the management of a bureaucracy. To further the success of this Integrity Zone, a Agent of Change was formed who acts as a Role Model for all parties. As a role model, agents of change must be able to provide examples in attitude, behavior, and thinking. In addition, they must be able to provide creative solutions in dealing with problems in their agencies. And must be able to provide creative ideas to improve the performance of all parties. Sometimes, their ideas are not well documented. So a digital-based system is needed that can facilitate the work of these agents of change. For this reason, it is necessary to create a Web-based application for recording, reporting, and implementing change agent performance so that it is more optimal, efficient, and effective. In this study, the development of web-based applications using a framework with PHP-Native technology. Most PHP frameworks that exist today, use MVC and OOP technology but no one has utilized PHP-Native technology as a framework. This aims to facilitate the creation of applications for programmers who have not familiar with MVC or OOP concept.
Comparison of K-Means and Self Organizing Map Algorithms for Ground Acceleration Clustering
Authors: Simamora Siska , Iqbal Muhammad , Siahaan Andysah Putera Utama , Khairul Khairul , Sitorus Zulham
Published: November 2024

This study evaluates earthquake-induced ground acceleration in Indonesia, which is located in the Pacific Ring of Fire zone, using Donovan's empirical method and comparing two clustering algorithms, Self Organizing Map (SOM) and K-Means. The main problem faced is the high risk of earthquakes in Indonesia and the need for effective methods to predict potential damage to buildings and infrastructure. The research objective is to evaluate earthquake-induced ground acceleration and identify acceleration distribution patterns using clustering techniques. The solution methods used include the application of the Donovan method to calculate ground acceleration based on BMKG data, as well as the use of SOM and K-Means algorithms to cluster the ground acceleration data. GIS and Python applications are used to visualize the clustering results. The results show that the Donovan method integrated with SOM and K-Means provides significant insights into the distribution of ground acceleration, thus assisting in risk evaluation, disaster mitigation planning, and the development of more effective earthquake-resistant infrastructure development strategies in Indonesia
A Mixed-Integer Programming Approach on Clustering Problems with Segmentation Application Customer
Authors: Elviana Arin , Rosmaini Elly , Nababan Esther Sorta Mauli
Published: November 2024

As a marketing strategy, segmentation involves categorizing customers into specific groups based on their loyalty to a brand. This process is crucial in shaping an effective business strategy, as identifying various customer types enables businesses to target their marketing efforts more precisely. This research focuses on solving the cluster optimization problem by applying a combinatorial optimization approach to develop a cluster optimization method. The combinatorial optimization utilized here operates on a binary system, using 0s and 1s to identify the optimal cluster for each object. Specifically, a value of 1 indicates that an object is assigned to an optimal cluster, while a value of 0 signifies that the object belongs to a non-optimal cluster. By designating clusters with a value of 1, the method ensures that the best optimization value is achieved. The 0-1 non-linear problem model ensures that objects with the shortest distances between them are grouped in the same cluster. Additionally, the model guarantees that each object belongs to only one cluster and that, across k tests, every cluster contains at least one object. This model can also be used to determine the ideal number of clusters for a given dataset, ensuring optimal segmentation results for business applications.
Complete Kernel Fisher Discriminant (CKFD) and Color Difference Histogram for Palm Disease
Authors: Perangin Angin Johanes Terang Kita , Herman Herman , Joni Joni
Published: November 2024

Palm oil plantations play a significant role in the economy of Indonesia, supporting 16.2 million people. However, plant diseases pose a major threat to the productivity and health of palm oil crops. Early detection of these diseases is essential to prevent yield losses and mitigate damage. This study proposes the application of the Complete Kernel Fisher Discriminant (CKFD) method combined with Color Difference Histogram to classify diseases affecting oil palm fronds and leaves. The CKFD method uses a non-linear kernel transformation to improve the performance of Fisher Linear Discriminant Analysis (FLDA), while the Color Difference Histogram enhances sensitivity to color variations in different lighting conditions. Experimental results demonstrate that the CKFD method achieves superior accuracy in disease detection compared to traditional Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The proposed approach showed an average accuracy of 94.5% for detecting diseases like Curvularia sp and Cochliobolus carbonus. The combination of CKFD with Color Difference Histogram significantly reduces the impact of lighting variations on the classification results, making it a robust solution for practical deployment in palm oil plantations. This research provides an effective tool for early disease detection and management in the palm oil industry.
Optimization of Stock Forecasting in Bali Retail Businesses to Support the Digital Economy Using Weighted Moving Average (WMA) Approach
Authors: Welda Welda , Dharsika I Gede Eka , Sarasvananda Ida Bagus Gede
Published: November 2024

The development of the digital economy provides new challenges for the retail sector, especially in stock management. Accurate stock management is a key factor in improving operational efficiency and minimizing the risk of overstock and understock. This research aims to optimize stock forecasting in retail businesses in Bali using the Weighted Moving Average (WMA) method. WMA gives greater weight to the most recent data in order to forecast future demand for goods. Sales data from 2017 to 2021 was collected and used as the basis for forecasting. The forecasting process was conducted for several products, including Dolphin and Dua Kelinci. The results show that WMA is able to provide accurate predictions, especially for products with stable demand patterns. For Dolphin products, the WMA forecast for January 2024 predicted a demand of 14.8 units, with a Mean Absolute Deviation (MAD) of 3.64. Dua Kelinci products, however, experienced more fluctuations in demand, with a forecasted January 2024 demand of 7.6 units and a MAD of 4.3. Despite some variations, WMA proved to be more accurate compared to simpler methods like Simple Moving Average (SMA). By using WMA, retailers can more efficiently manage stock, improve customer satisfaction, and reduce the risk of overstocking or understocking. This research confirms the importance of integrating advanced forecasting methods in supporting the competitiveness of the retail sector in the digital economy era.
Comparative Study of XGBoost, Random Forest, and Logistic Regression Models for Predicting Customer Interest in Vehicle Insurance
Authors: Airlangga Gregorius
Published: November 2024

In today’s competitive insurance market, accurately predicting customer interest in additional products, such as vehicle insurance, is crucial for optimizing marketing strategies and maximizing sales. This study presents a comparative analysis of three machine learning models such as XGBoost, RandomForest, and Logistic Regression to predict customer interest in vehicle insurance based on a dataset that includes demographic, vehicle, and policy-related features. The dataset was analyzed using five-fold cross-validation, and the performance of the models was evaluated using AUC-ROC, precision, recall, and F1-score. XGBoost demonstrated the highest recall (0.9525) and AUC-ROC (0.7854), making it the most effective model for identifying customers interested in vehicle insurance, though at the expense of lower precision (0.2585). RandomForest showed a more balanced trade-off between precision (0.3064) and recall (0.5341) but performed lower overall. Logistic Regression, while the most interpretable model, exhibited high variability in performance across different folds, with a lower average precision (0.2372). The findings of this research highlight that XGBoost is ideal for maximizing recall in high-volume campaigns, while RandomForest may be better suited for applications requiring fewer false positives. These results offer valuable insights into model selection based on business objectives and resource allocation.
Reclaiming Voices: Identity Formation and Agency in Dalit Women's Autobiographical Literature
Authors: Zorat Emma
Published: November 2024

This paper investigates the intersection of education literature and identity among Dalit women in India, highlighting their historical and contemporary struggles against caste-based discrimination and gender inequality. It examines the evolving access to education for Dalits, noting persistent gaps despite policy changes. The study critically analyzes how Dalit women have been portrayed in colonial literature and by Dalit men, revealing a recurring double marginalization and stereotypical depictions that underscore patriarchal and caste oppressions. Contrastingly, the rise of Dalit women’s autobiographical literature marks a significant departure, asserting their agency and challenging prevailing narratives. This literature not only voices the unique challenges faced by Dalit women but also reclaims their narrative space, contributing profoundly to the discourse on Dalit identity and feminism. The paper underscores the need to acknowledge these intersectional experiences to advance justice and equality in Indian society.
Marital Status, Ethnicity, and Cardiovascular Health: A Systematic Literature Review
Authors: Houston Eric , Gonzalez-Valentino Omar , Song Gaole , LaRue Evans Justine
Published: November 2024

Objective: The association between marital status and health has long been recognized albeit not without controversy. While substantial empirical research provides evidence that being married confers health benefits, there is evidence that heterogeneity exists in these benefits based on sociodemographic factors, including race/ethnicity, gender, and income. Given continued disparities in a range of health outcomes, there is a need to identify and better understand variations in the health advantage of being married versus unmarried (i.e., never-married, married, widowed, separated, or divorced). In this systematic literature review, we aimed to examine racial/ethnic variations in the purported protective role played by marital status with regard to cardiovascular disease (CVD) risk factors and CVD outcomes. The review focused on studies that included population groups highly vulnerable to poor cardiovascular health. Design: We conducted a search of MEDLINE, PubMed, PsycINFO, and lists of relevant articles published during a five-year period in English. Databases were searched using Medical Subject Headings (MeSH) terms and other appropriate keywords related to marital status, race/ethnicity, and CVD risk factors and outcomes. Four reviewers independently screened titles and abstracts and abstracted data from full-text articles. Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Results: The search yielded 4,577 titles, and 18 studies were included in this review. Studies showed mixed findings on the relationship between marital status, race/ethnicity, and CVD risk factors. With regard to CVD outcomes, most studies reported findings that suggest being married may serve as a protective factor across race/ethnic categories. Conclusion: Some cardiovascular risk factors and disease outcomes vary by the intersection of marital status and race/ethnicity. Differences in the relationship between marital status, race/ethnicity, and cardiovascular health need to be examined further in future research. These differences should be considered in the development of interventions aimed at addressing health disparities.
Intrasaccular Device Occlusion and Location Scale IDOL - a Novel Grading Scale For Cerebral Aneurysms Treated with Intrasaccular Devices
Authors: Killer-Oberpfalzer Monika
Published: November 2024

Background/Aims: Intrasaccular devices are an emerging treatment for cerebral aneurysms. However, current grading scales for outcome assessment are difficult to apply as device positioning is not taken into account. We present a novel grading scale to assess how likely a complete occlusion is predictale for cerebral aneurysms treated with intrasaccular devices. Materials: The scale was developed using results from 143 aneurysms treated at our institution with intrasaccular devices from 2019 to 2023. Angiographic images and clinical complications were taken to illustrate key aspects of the scale. Results: The scale considers device position relative to the parent artery and the aneurysm wall, contrast filling, neck coverage, and contrast inflow/stability. Conclusions: This scale helps standardize outcome measurements in accordance with the Modified Raymond–Roy Classification and O`Kelly Marotta grading scales, providing a basis for the common reporting of results.
Pemberdayaan Masyarakat Desa Wisata Melalui Pelatihan Pengelolaan Homestay dan Kebersihan
Authors: Jaya Risman , Ohyver Daniel Adolf , Muhtasom Ali , AB Ahmad
Published: November 2024

Pelaksanaan kegiatan pengabdian masyarakat di Desa Wisata Andalan Lembanna bertujuan untuk meningkatkan keterampilan dan pengetahuan masyarakat dalam mengelola homestay, menjaga kebersihan destinasi wisata, dan mengembangkan potensi ekonomi lokal. Kegiatan ini melibatkan persiapan yang matang, observasi lapangan, dan pelaksanaan yang melibatkan ceramah, diskusi, serta praktik langsung di homestay. Materi pelatihan mencakup pengelolaan homestay, pelayanan tamu, praktik kebersihan, pengolahan sampah, dan pemasaran homestay secara digital. Evaluasi proses pelatihan menunjukkan bahwa materi pelatihan dinilai relevan, metode pelatihan efektif, dan partisipasi peserta baik. Kesimpulannya, kegiatan tersebut dianggap berhasil dan diharapkan dapat mendukung pengembangan pariwisata berkelanjutan dan pemberdayaan masyarakat lokal di desa wisata lainnya
Bimbingan Teknis Kriteria ADWI: Pemetaan Desa Wisata Baloli, Luwu Utara
Authors: Ery Anda Prasetyo , Darmayasa Darmayasa , Susanto Yenny , Latif Abdul
Published: November 2024

Kompetensi SDM unggul di desa wisata masih rendah, infrastruktur dan fasilitas pendukung yang masih minim, kesiapan konten dan promosi yang masih konvensional. Adapun tujuan di selenggarakannya bimbingan teknis kepariwisataan adalah memberikan pendampingan dalam meningkatkan kualitas pelayanan pengelola desa wisata dengan cara mendorong SDM pengelola desa wisata untuk mengembanmgkan diri dengan menambah pengetahuan dan pengalaman dalam pengelolaan desa wisata. Metode dalam pelaksanan bimtek adalah terdiri dari tahap awal, tahap implementasi, dan tahap akhir. Data diperoleh dari hasil pemetaan yang dilakukan oleh peserta bimtek. Kesimpulan yang didapatkan adalah pentingnya bimbingan teknis di setiap desa wisata untuk meningkatkan serta menerapkan pengetahuan dan keterampilan dalam pengelolaan desa wisata, melalui bimbingan teknis ini para pengelola desa wisata dapat menggali potensi yang ada di desa wisata, Dari hasil bimbingan teknis ini diharapkan desa wisata di Luwu Utara dapat memenuhi kriteria dalam ADWI 2023.
Bimbingan Teknis Pemanduan dan Penyusunan Paket Wisata Bahari Kabupaten Barru, Sulawesi Selatan
Authors: Wadid Rante Margaretha , Renold Renold , Machmury Andri , Rahayu Mia , Gunawan Indra , Rifqoh Hiro
Published: November 2024

Kabupaten Barru, Sulawesi Selatan, memiliki potensi pariwisata yang signifikan yang meliputi alam, budaya, sejarah, bahari, dan buatan. Pengembangan pariwisata di wilayah ini berkontribusi pada peningkatan ekonomi daerah. Namun, seperti dilaporkan oleh penelitian sebelumnya, terdapat dampak positif dan negatif yang perlu diperhatikan. Pengembangan pariwisata juga memerlukan kolaborasi antara pemerintah, institusi pendidikan, dan masyarakat setempat. Program pengabdian kepada masyarakat yang dilaksanakan oleh Politeknik Pariwisata Makassar bertujuan untuk meningkatkan keterampilan dan pengetahuan masyarakat dalam bidang pariwisata, khususnya dalam teknik pemanduan wisata dan penyusunan paket wisata. Melalui program ini, masyarakat setempat dapat lebih memahami dampak dari keterlibatan mereka dalam pengembangan sektor pariwisata. Metode penyampaian materi yang interaktif dan memperhatikan kebutuhan peserta menjadi kunci keberhasilan program ini. Penyusunan paket wisata juga menjadi aspek penting dalam pengembangan pariwisata, di mana para peserta kegiatan dapat memperoleh pengetahuan dan keterampilan yang dibutuhkan.  Kesimpulannya, melalui kolaborasi antara berbagai pihak dan penerapan metode yang efektif, potensi pariwisata di Kabupaten Barru dapat dimaksimalkan untuk memberikan manfaat ekonomi, sosial, dan lingkungan yang berkelanjutan bagi masyarakat setempat.
Pelatihan Pengolahan Minuman Berbahan Lokal Menjadi Produk Bernilai Ekonomi di Desa Margacinta, Pangandaran
Authors: Iskandar Ridwan , Syadad Saefullah Moch. Agus , Rusmana Odang
Published: November 2024

This community service activity aims to improve basic knowledge of skills in making drinks from plantations and agriculture that grow around the village, as well as to improve the entrepreneurial spirit for managers of Village-Owned Enterprises (BUMDES) and Tourism Awareness Groups (Pokdarwis) in Margacinta Village, Cijulang District, Pangandaran Regency. The activity was carried out at the Karangkamal Hamlet Hall and was attended by 25 participants. The delivery method was carried out using interactive and demonstrative counseling techniques, with the aim of transforming knowledge, attitudes and skills as well as achieving prosperous village communities. The results of the activity showed an increase in the skills and understanding of the participants regarding the utilization and knowledge of plantations and agriculture, namely coconut and honje, which are processed into mixed drinks with the hope that the benefits, uses, processing and results of the drinks contained therein can be obtained. as well as in terms of processing plantation and agricultural products from the garden to being served in a glass, as well as the motivation and follow-up of participants in making, producing and even consuming their own beverage products along with the utilization of other commodities have been successfully improved through this activity.
Life table Analysis and Extent of Damage Caused by Spodoptera exigua (Noctuidae: Lepidoptera), A Serious Pest
Authors: Maalik Sadia , Mushtaq Sajida , Ain Quratul , Batool Moazama , Bano Naheed , Hafeez Ayesha
Published: November 2024

Agriculture is the baseline for uplift of economic sources for any country. But every year many hectares of the cropland area are damaged by herbivore insects. Among these herbivore insects, Spodoptera exigua is a serious pest of agricultural land. It damages almost 90 plant species. In the present study analysis of each life stage of S. exigua was carried out by providing four host plants namely, Cauliflower, Cabbage, Brassica and Turnip. Experiment was conducted at 20 ±5˚C temperature, 60±5 relative humidity and 16:8 light and dark duration. Total food consumption was 14.01 ± 0.61 g on cauliflower, 11.65±0.07g on Cabbage, 5.41±0.19 on Brassica and 4.92±0.5 on turnip. Maximum larval duration was also recorded on cauliflower (14.00±0.47 days). The longest pupal duration was recorded for the larvae fed on turnip (14.00±0.82 days). Overall cauliflower was significantly preferred food (p0.001) for S. exigua. The current study is a prerequisite for establishing effective control measure against this serious pest.
Structural correspondence in Molyneux’s subjects
Authors: Cheng Tony
Published: November 2024

The historical Molyneux’s question – roughly, whether congenital blind subjects can visually identify shapes in front of them right after being made to see – is having its renaissance in recent years (Ferretti and Glenney, 2021). While there have been many different formulations of it, and many attempted answers as well, no clear consensus has been reached. Moreover, although arguably both memory and imagination are involved in the process, their roles in the Molyneux’s task have not been adequately discussed. In this paper, I focus on a specific version of Molyneux’s question, propose a specific hypothesis in relation to that version, and sketch the roles of structural correspondence, memory, and imagination in the relevant hypothesis. One key moral of this discussion is that while Molyneux’s question has primarily been regarded as a perceptual puzzle, other kinds of mental episodes such as memory and imagination have to be taken into account as well in order to have a more satisfying answer to versions of Molyneux’s question (Macpherson and Dorsch, 2018).
Pattern recognition receptors in Crustacea: immunological roles under environmental stress
Authors: Betancourt Jesús Luis , Rodríguez-Ramos Tania , Dixon Brian
Published: November 2024

Innate immunity is the first line of defense against infections and the only known available strategy for invertebrates. Crustaceans, being mostly aquatic invertebrates, are constantly exposed to potential pathogens in the surrounding water. Their immune system abolishes most microbes that enter and are recognized as a threat. However, the stress produced by high population densities and abiotic changes, in aquaculture, disrupts the host-pathogen balance, leading to severe economic losses in this industry. Consequently, crustacean immunology has become a prime area of research where significant progress has been made. This review provides our current understanding of the key pattern recognition receptors in crustaceans, with special focus on Decapoda, and their roles in triggering an immune response. We discuss recent developments in the field of signal transduction pathways such as Toll-like receptors (TLRs) and the immune deficiency (IMD) pathway, and examine the role of antimicrobial peptides (AMPs) in pathogen defense. Additionally, we analyze how environmental stressors—such as temperature fluctuations, ammonia levels, and pollution—impact immune responses and increase susceptibility to diseases. Finally, we highlight future research directions, emphasizing the need to explore the interactions between environmental stressors and immune signaling pathways and to develop strategies to enhance immune responses in crustaceans within aquaculture settings. Altogether, these advancements deepen our understanding of pathogen recognition in invertebrates and the specific defense mechanisms employed by crustaceans, particularly in response to infections triggered by pathogens under abiotic stressors.
Remote sensing inversion of suspended particulate matter in the estuary of the Pinglu Canal in China based on machine learning algorithms
Authors: Mo Jinying , Tian Yichao , Wang Jiale , Zhang Qiang , Zhang Yali , Tao Jin , Lin Junliang
Published: November 2024

Introduction

Suspended particulate matter (SPM) is a critical indicator of water quality and has a significant impact on the nearshore ecological environment. Consequently, the quantitative evaluation of SPM concentrations is essential for managing nearshore environments and planning marine resources.

Methods

This study utilized Sentinel-2’s single band and water index variables to develop a remote sensing inversion model for oceanic SPM in the estuary of the Pinglu Canal in China. Six machine learning algorithms were employed: K-nearest neighbor regression (KNNR), AdaBoost regression (ABR), random forest (RF), gradient boosting regression (GBR), extreme gradient boosting regression (XGBR), and light generalized boosted regression (LGBM). The model with the optimal performance was then selected for further analysis. This research applied the established model to investigate the spatial-temporal dynamics of SPM from 2021 to 2023.

Results

The findings indicated that (1) the XGBR algorithm exhibited superior performance (R2 = 0.9042, RMSE = 3.0258 mg/L), with LGBM (R2 =0.8258, RMSE = 4.0813 mg/L) and GBR (R2 = 0.823, RMSE = 4.3477 mg/L) also demonstrating effective fitting. However, the ABR, RF, and KNNR algorithms produced less satisfactory fitting results. (2) Additionally, the study revealed that the combination of input variables in the XGBR algorithm was more accurate than single-variable inputs. (3) The contribution of single-band variables to the XGBR algorithm surpassed that of water index variables, with B12, B4, and B11 emerging as the top three influential variables in the model. (4) The annual SPM concentration in the study area exhibited an overall increasing trend, while its spatial distribution generally decreased from the estuary toward the Maowei Sea and Qinzhou Bay.

Discussion

The combination of Sentinel-2 data and XGBR model has shown good performance in retrieving SPM concentration, providing a new method and approach for large-scale estimation of SPM concentration.
A hypergraph transformer method for brain disease diagnosis
Authors: Han Xiangmin , Feng Jingxi , Xu Heming , Du Shaoyi , Li Junchang
Published: November 2024

Objective

To address the high-order correlation modeling and fusion challenges between functional and structural brain networks.

Method

This paper proposes a hypergraph transformer method for modeling high-order correlations between functional and structural brain networks. By utilizing hypergraphs, we can effectively capture the high-order correlations within brain networks. The Transformer model provides robust feature extraction and integration capabilities that are capable of handling complex multimodal brain imaging.

Results

The proposed method is evaluated on the ABIDE and ADNI datasets. It outperforms all the comparison methods, including traditional and graph-based methods, in diagnosing different types of brain diseases. The experimental results demonstrate its potential and application prospects in clinical practice.

Conclusion

The proposed method provides new tools and insights for brain disease diagnosis, improving accuracy and aiding in understanding complex brain network relationships, thus laying a foundation for future brain science research.
MinD2 modulates cell shape and motility in the archaeon Haloferax volcanii
Authors: Patro Megha , Grünberger Felix , Sivabalasarma Shamphavi , Gfrerer Sabrina , Rodriguez-Franco Marta , Nußbaum Phillip , Grohmann Dina , Ithurbide Solenne , Albers Sonja-Verena
Published: November 2024

In bacteria and archaea, proteins of the ParA/MinD family of ATPases regulate the spatiotemporal organization of various cellular cargoes, including cell division proteins, motility structures, chemotaxis systems, and chromosomes. In bacteria, such as Escherichia coli, MinD proteins are crucial for the correct placement of the Z-ring at mid-cell during cell division. However, previous studies have shown that none of the 4 MinD homologs present in the archaeon Haloferax volcanii have a role in cell division, suggesting that these proteins regulate different cellular processes in haloarchaea. Here, we show that while deletion of MinD2 in H. volcanii (∆minD2) does not affect cell growth or division, it impacts cell shape and motility by mispositioning the chemotaxis arrays and archaellum motors. Finally, we explore the links between MinD2 and MinD4, which has been previously shown to modulate the localization of chemosensory arrays and archaella in H. volcanii, finding that the two MinD homologues have synergistic effects in regulating the positioning of the motility machinery. Collectively, our findings identify MinD2 as an important link between cell shape and motility in H. volcanii and further our understanding of the mechanisms by which multiple MinD proteins regulate cellular functions in haloarchaea.
Deciphering pain: molecular mechanisms and neurochemical pathways–challenges and future opportunities
Authors: Dehghan Bahar , Abolhasanzadeh Narges , Shademan Behrouz , Nourazarian Alireza
Published: November 2024

This review delves into the intricate biological underpinnings of pain perception. It encompasses nociceptive signaling pathways, the molecular mechanisms involved, and the subjective experience of discomfort in humans. The initial focus is on nociceptor transduction, where specialized neurons transform noxious stimuli into electrical impulses. Subsequently, the review explores the central nervous system, elucidating how these signals are processed and modulated by critical elements such as ion channels, receptors, and neurotransmitters (e.g., substance P, glutamate, GABA). Shifting gears toward chronic pain, the review examines the concept of neuroplasticity, highlighting its potential to induce maladaptive responses through alterations in neural networks. The burgeoning field of pain genomics, alongside established genetic research, offers valuable insights that could pave the way for a framework of personalized pain management strategies. Finally, the review emphasizes the significance of these molecular insights in facilitating accurate therapeutic interventions. The overarching objective is to establish an integrative framework for precision medicine in pain management by incorporating this information alongside biopsychosocial models. This framework serves to translate the heterogeneous landscape of pain mechanisms into a coherent roadmap for the development of effective therapies.
18F-FDG PET/CT detection of primary mammary-like adenocarcinoma of the vulva with multiple metastases: a case report
Authors: Song Qing , Zhang Biyun , Gu Yu
Published: November 2024

Mammary-like adenocarcinoma of the vulva is a malignancy with a low incidence rate compared with the squamous cell carcinoma occurring at the same site. We present a rare case of mammary-like adenocarcinoma of the vulva with multiple-organ involvement using 18F-FDG PET/CT. This study indicates that 18F-FDG PET/CT can not only detect the primary lesion but also distinguish the stage of the mammary-like adenocarcinoma of the vulva.