bluuebunny/crossref_metadata_2025 huggingface dataset:
Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model
Authors: Haiqin Zhu
Published: May 2024

Polar ice sheets, or ice cores, are among the most well-known natural archives that might provide crucial historical details about our planet's previous environment. An important factor in establishing the fundamental characteristics of ice, likes pore close-off, albedo, melt events, is the ice-core microstructure. To engulf these complications Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model (ICMCTS-WSOA-DSSDNN-GMM)is proposed. Initially, micro scale CT images are collect from Alfred Wegener Institute ice-core storage as input. Then,data’s are given to pre-processing. In pre-processing, it enhance image brightness, remove salt pepper noise, crop outer ring (carbon fiber casing) to have only ice particles in image utilizing Federated Neural Collaborative Filtering (FNCF).The pre-processing output is given to segmentation. Here, micro scale CT image is utilized for segmenting high-resolution scans using Gaussian Mixture Model (GMM). After that, the segmented images are given to Dual Stream Spectrum Deconvolution Neural Network optimized with Water Strider Optimization Algorithm for classifying micro scale CT images as sintered snow, compacted firn and bubbly ice. The proposed ICMCTS-WSOA-DSSDNN-GMM method is executed in python. The performance of ICMCTS-WSOA-DSSDNN-GMM approach attains 16.24%, 17.90% and 27.7% high accuracy, 14.04%, 25.51% and 19.31% higher precision and 14.36%, 12.65%, 14.51% higher recall analyzed with existing techniques likes Ice-Core Micro-CT Image Segmentation With Deep Learning and Gaussian Mixture Method (ICMCTS-U-net-GMM), Computer Aided Detection of COVID 19 from CT Images Depend on Gaussian Mixture Method with Kernel Support Vector Machines Classifer (ICMCTS-KNN-GMM) and the Gmmseg: Gaussian mixture based generative semantic segmentation methods(ICMCTS-FCN-GMM) respectively.
Massive Open Online Course (MOOCs) English Courses Based On Hamiltonian Deep Neural Network
Authors: Deyi Li
Published: May 2024

Numerous researchers are now concentrating on the English course on intercultural communication. Notwithstanding, these courses encounter several obstacles, including student variations in traditional English for cross-cultural instruction, developing students' cross-cultural communication skills, and improving the quality of instruction.  To overcome these problems, in this manuscript, a Hamiltonian deep neural network (NN) approach is proposed for improving the teaching quality of massive open online course (MOOCs) based English course. Initially, data is given from English-language massive open online courses (MOOCs) teaching adult basic life support (ABLS) dataset. Afterward the data is fed to Switching Hierarchical Gaussian Filter. The pre-processing output is provided to the Hamiltonian Deep Neural Network (HDNN) used to improve the teaching quality of MOOC English course. The learnable of the optimized is using Coati Optimization Algorithm (COA). The proposed technique is executed in Python and the effectiveness of the proposed MOOCEC-HDNN method is improved by using various performances evaluating metrics like, teaching Efficiency, Robustness Gain, teaching quality and system Efficiency are analysed. The proposed MOOCEC-HDNN method is shows the higher teaching efficiency of 78%, Robustness Gain of 28dB and teaching quality 68% while comparing other existing methods such as massive open online course (MOOCs) based English course based on Gannet algorithm and Neural Network (MOOCEC-GA-NN), MOOCs based English course based on K-modes algorithm and Neural Network (MOOCEC-KMA-NN) and MOOCs based English course based on Conventional Neural Network (MOOCEC-CNN), respectively.
Optimized Multi-Scale Mixed Dense Graph Convolution Network for Career Impression Management in College Students Based on Social Network Analysis
Authors: Zhelin Li
Published: May 2024

This investigation attempts to increase success rate of employment, entrepreneurship by addressing the dangers that college students face when seeking work and starting their own businesses in rural areas. It offers a way of social environment analysis based on employment, entrepreneurship of college students in rural areas. This approach comprehends the employment and social environment of college students' rural employment, entrepreneurship as research goal. It thoroughly examines, detects component parts of the college students' rural employment, entrepreneurship environment. In this manuscript, Optimized Multi-Scale Mixed Dense Graph Convolution Network for Career Impression Management in College Students Based on Social Network Analysis (MSMDGCN-CIM-CS-SNA) is proposed. Initially input data are gathered from Xing social network Dataset. To execute this, input data is pre-processed using Distributed Adaptive Cubature Information Filtering(DACIF) and it removes the noise from collected data; then the Pre-processed data are fed to MSMDGCN for effectively categorize Career Impression Management in College Students. In general, MSMDGCN does not express adapting optimization strategies to determine optimal parameters to ensure accurate Career Impression Management in College Students. Hence, the Sand Cat swarms optimization(SCSOA) to optimize Quantum Conditional Generative Adversarial Network which accurately Career Impression Management in College Students based on Social Media.Then the proposed MSMDGCN-CIM-CS-SNA is implemented and the performance metrics like Student Origin; Proportion of Daily Living Expenses, Student’s Graduation Trend, Selection of Career Areas for College Students, Units of College Student’s Career Intention and Computation Time the World are analyzed. Performance of the MSMDGCN-CIM-CS-SNA approach attains 18.75%, 26.89% and 32.57% higher Student’s Graduation Trend; 16.87%, 24.57% and 32.94% lower Units of College Student’s Career Intention and 18.43%, 25.64% and 31.40% lower Computation Time when analyzed through existing techniques like analysis on social environment of college students’ rural employment with entrepreneurship (ASE-CS-REE), recommendation model for college career entrepreneurship projects depend on deep learning (RM-CCE-DL), group relationship mining of college students depend on predictive social network (GRM-CS-PSN) methods respectively.
Visualization and Interaction Model of Outdoor Landscape Design Based on Virtual Reality Technology using Optimized Sparse Spectra Graph Convolutional Neural Network
Authors: Yue Song
Published: May 2024

The artistic thought of traditional garden landscape design has significant effect on garden art, architectural theory, gardening theory are also inextricably linked. In modern gardens, traditional architecture lacks applicability to construction of garden painting environment. In this Manuscript, Visualization and Interaction Model of Outdoor Landscape Design Based on Virtual Reality Technology using Optimized Sparse Spectra is Graph Convolutional Neural Network is proposed (VIM-OLD-VRT-SSGCNN).Initially the data is collected fromArcbazer.com data. Afterwards sparse spectra graph convolutional neural network (SSGCNN) is used to design the Outdoor Landscape. In Generally, SSGCNN doesn’t expose some adoption of optimization systems for calculating optimal parameters for exactly design the Outdoor Landscape. Hence, Piranha Foraging optimization Algorithm (PFOA) is proposed to optimize SSGCNN which precisely design Outdoor Landscape. Finally Visualization and Interaction Model of Outdoor Landscape Designed. The proposed VIM-OLD-VRT-SSGCNN method is implemented python and performance metrics likes accuracy, precision, F1-score, sensitivity, specificity, computational time, RoC are evaluated. The performance of VIM-OLD-VRT-SSGCNN method provides 18.31%, 20.72%, 21.67% greater accuracy, 17.83%, 18.42%, 20.38% greater precision and 21.75%, 22.36%, 23.59% greater computational time while compared with existing techniques like Utilizing Deep Learning to Generate Font with Backyards in Landscape Architecture(UDL-GFB-LA), Urban Landscape Design Depend on Data Fusion with Computer Virtual Reality Technology (ULD-DF-CVRT),perception of urban sounds cape and landscape utilizing different visual environment reproduction techniques in virtual reality (PUSL-DVER-VR) respectively.
Analyse Chinese Teaching Project-Based Learning Design Application Optimization Using Keras Model
Authors: Weihua Bai
Published: May 2024

Educational methods for data mining may be used to successfully analyze data from a variety of educational platforms, including e-learning, e-admission programs, and automated results management solutions, to provide very helpful insights into students' performance. Chinese language teaching will reach new heights as a result of the rapid advancement of network technology and the constant improvement of network transmission speed. Additionally, the acceptance of online payment methods is growing, simplifying international and web-based trade. Deep learning models have become more significant in many spheres of life recently. It made it possible for investigators to automatically extract superior characteristics from unprocessed data.With the aid of the Keras model, make it is easier for students to rapidly obtain practical knowledge with deep learning structures and boost their confidence and enthusiasm for learning. In this study, we investigate the Long Short-Term Memory (LSTM) deep neural network model with Novel Swarm Optimization (NSO) network to accurately forecast student improvement using historical data. The most cutting-edge LSTM and an optimization mechanism model (NSO) have been employed in this work to solve research issues based on sophisticated feature categorization and prediction. For Chinese academics, institutions, and government agencies to accurately anticipate performance, this study is very important. When paired with the optimization mechanism (NSO), LSTM's better sequence learning capabilities provide performance that is superior to the current state-of-the-art. The prediction accuracy of the suggested approach is 93%.
Multi Objective Optimization Framework to Provide Reactive Power Support to the Local Distribution Grid Using a Heuristic Approach.
Authors: Jaydeepsinh Sarvaiya
Published: May 2024

Reactive power support to the local distribution grid becomes mandatory as it highly impacts the network's voltage profile and active power loss. Reactive power support improves the performance and efficiency of the grid. However, higher compensation to the line increases losses, deteriorates the voltage profile, and occupies the investment cost. The location of these reactive power compensators (RPC) also influences the network's performance. This paper presents a multi-objective strategy for optimal reactive power compensation to get techno-economic benefits. An objective function, which includes technical and economic criteria, has been formulated. The proposed MOF includes weighted distributed objectives that are minimized to get a trade-off solution. The proposed MOF has been minimized using three different heuristic algorithms, i.e. TLBO, GA and PSO. The proposed strategy has been tested on 56 buses 11 KV Gurukul distribution network located at Bhavnagar, Gujarat, India. NR load flow has been utilized to investigate losses and bus voltages.
Independent Video Steganalysis Framework Perspective of Secure Video Processing
Authors: Bilal A. Ozturk
Published: May 2024

We have designed an algorithm of cross-domain feature set extraction for cross-domain video steganalysis of video steganography in multiple domains. For video steganography, we implemented the recently proposed steganography methods such as PMs, MVs, and IPMs. The outcomes of these techniques have fed as input to the proposed Steganalysis approach. We have designed a cross-domain technique for Steganalysis in which the global feature set is extracted according to common statistical properties. After the extraction of global features, the domain-specific features have been extracted in the local features set. Both global and local features are set to form the cross-domain steganalysis technique. The classification has been performed by using the conventional machine learning classifiers such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).
Exploring Medium and Low-Temperature Convective Geothermal Resources Through Controlled Source Audio Magnetotelluric Mothod
Authors: Sheng He
Published: May 2024

Medium and low-temperature convective geothermal resources are a crucial component of China's geothermal wealth, primarily concentrated in the eastern coastal regions. To maximize the utilization of these resources, geophysical surveys are essential, with controlled source audio magnetotelluric(CSAMT) being a prominent and effective method employed in geothermal resource assessments. This study focuses on applying CSAMT to survey specific geothermal sites, including the Xiu Yan Gou Tang Hot Spring, Fengcheng Gou Tang Hot Spring, and Hai Cheng Xi Huang di Geothermal Field. The results obtained through this method provide detailed insights into the distribution of both deep and shallow thermal storage spaces within these fields, shedding light on the intricate thermal circulation systems associated with them. These findings serve as a reliable foundation for planning and executing further geothermal resource development projects. CSAMT emerges as a comprehensive tool for understanding the nuances of medium and low-temperature convective geothermal resources.
The State of Agricultural Development and Industrial Prosperity Pathway and Strategy Design in the Context of Data-Driven and Rural Revitalization
Authors: Changlin Wang
Published: May 2024

Current advances in materials science are providing a huge variety of novel adsorbents that could be used for the transformation and storage of low-temperature heat, such as the heat generated by renewable energy sources.Achieving industrial prosperity is a key step in building an all-round well-off society and realizing agricultural modernization. In the process of industrial prosperity, there are also many problems: firstly, the long-term traditional farming in China has led to the information blockage and ideological constraint of most farmers in China, so that they are slow to accept new things and new ideas; secondly, due to the "one long and one short" characteristic of long growth cycle and short storage cycle of agricultural products, it is difficult for rural transportation and deep processing of agricultural products. Secondly, because of the "long and short" nature of the long growth cycle and short storage cycle of agricultural products, the demand for rural transportation and deep processing of agricultural products is high. This study is based on the problems related to sustainable agricultural development in traditional agricultural areas. The main purpose of this study is to clarify the sustainable development status of agricultural systems under different agricultural development paths in traditional agricultural areas, to establish a methodological system for studying regional agricultural development paths, to explore the optimal path for sustainable regional agricultural development, and orderly management of sustainable agricultural development in traditional agricultural areas, so as to promote the sustainable development of traditional agricultural areas with economic prosperity, social stability and ecological health. The experimental results prove that the optimized SGA algorithm has carried out good system dynamics modeling for the typical traditional agricultural area - the problem of choosing the agricultural development path, and the comprehensive evaluation of the simulation results.
Analysis of Parenting Stress, Co-parenting Level, Family Intimacy and Adaptability on Children's Social Skills via self-attention Network
Authors: Sisi Zhao
Published: May 2024

This paper uses the self-attention method to track family intimacy.  We propose the Self-Attention Adversarial Deep Subspace Clustering Algorithm (SAADSC). The self-attention adversarial network is used to impose a prior distribution constraint in the feature learning of the autoencoder, which guides the learned feature representation to be more robust, thereby improving the clustering accuracy. The results show that the proposed algorithm outperforms the state-of-the-art methods in terms of accuracy (ACC) and standard mutual information (NMI). Therefore, we can effectively transfer this self-attention method to the analysis of the effects of adaptation on children's social skills.
Exploring the Relationship between Solar Panel Adoption and Socio-Economic Factors: A Case Study of Urban Households in Developing Countries
Authors: Qashtalani Haramaini
Published: May 2024

This article will discuss the results of research on how the use of solar panels as an alternative energy source is related to social and economic factors, especially in urban areas in developing countries. This study will analyze factors such as education level, income, and people's perceptions of alternative energy to identify correlations between solar panel use and socio-economic variables. It is hoped that this article can provide new insights for public policy in promoting the use of renewable energy and creating a more inclusive society. This research can be categorized as quantitative. The research method used is the associative research method. The data analysis method used is correlation analysis. The sampling technique used is purposive sampling. The results of the study show that there are different characteristics of the relationship between the use of solar panels and socio-economic variables. Research on the relationship between global solar panel use and factors such as the number of prosumers, total kwh exports, GRDP, total investment, total load, home charging ev, distance to the capital city, air quality, and per capita expenditure has very important uses for stakeholders, especially in terms of sustainable energy development and reducing negative impacts on the environment. Policymakers can use the results of this research to formulate more appropriate policies to promote the use of solar energy and drive the shift to renewable energy sources.
Cyclone Intensity Estimation Using Deep Learning
Authors: D. Yella Krishna
Published: May 2024

Cyclone Intensity Estimation based on Deep Learning focuses on cyclone intensity estimation, enhancing early warning systems and empowering decision-makers to take proactive measures to safeguard vulnerable communities and infrastructure, which aims to minimize the devastating impacts of cyclones worldwide. The challenge in cyclone intensity prediction persists due to the complex and dynamic nature of these storms. Traditional methods fall short of capturing rapid changes. This research project leverages the power of deep learning to enhance the accuracy of cyclone intensity prediction by utilizing both satellite images and grayscale representations as input datasets. It involves preprocessing and feature extraction from satellite images captured during cyclonic events. Convolutional Neural Networks (CNN) are employed to automatically learn and extract relevant patterns from these complex datasets. In parallel, grayscale images derived from the original satellite images are utilized to capture essential structure information that contributes to cyclone intensity. The fusion of information from both modalities is achieved through a novel deep learning patterns that go beyond the capabilities of traditional intensity estimation methods, and architecture, fostering a comprehensive understanding of cyclonic patterns that go beyond the capabilities of traditional intensity estimation methods. A new approach seeks to automate cyclone estimation, streamlining timelines and increasing efficiency by merging deep learning with hurricane-focused satellite data. This approach might result in more precise forecasts, Reducing the amount of casualties and property damage in prone areas.
Analysis and Measurement of Supraharmonics in Real Textile Industries Using Hybrid Technique
Authors: Y. Dhayaneswaran
Published: May 2024

In this paper proposes a hybrid ADKF-FHO method for analysis and study of supraharmonics in textile industry. The proposed hybrid approach is the combined performance of anisotropic diffusion Kuwahara filtering (ADKF)and Fire Hawk Optimizer (FHO). Commonly it is named as ADKF-FHO technique. The major objective of the proposed approach is the main aim is to analysis of Supraharmonicsin real textile industries. The Variable frequency drive is predominantly used in Textile industry for varying the speed to control the roller speed for obtaining quality of yarn from the machine with more productivity and high energy saving. The ADLF is utilized to ensure the supraharmonics in textile industry and the FHO is used to optimizing the switching states of the embedded power converter. In this paper, standards and its limits are discussed; simulation model of the drive is done to ensure the Supraharmonics. Real time testing of the motor with drive for ensuring the Supraharmonics at testing laboratory and real time Textile industry in different geographical location is discussed in detail. By then, the proposed model is implemented in MATLAB/Simulink working stage and the execution is calculated with the existing methods. The proposed method shows better results in all approaches like RFA, CSA and PSO.
A Methodology for Speaker Diazaration System Based on LSTM and MFCC Coefficients
Authors: Indu D.
Published: May 2024

Research on Speaker Identification is always difficult. A speaker may be automatically identified using  by comparing their voice sample with their previously recorded voice, the machine learning strategy has grown in favor in recent years. Convolutional neural networks (CNN) , deep neural networks (DNN)  are some of the machine learning techniques that  has employed recently. The article will discuss a successful speaker verification system based on the d-vector to construct a new approach based on speaker diarization. In particular, in this article, we use the concept of LSTM to cluster the speech segments using MFCC coefficients and identify the speakers in the diarization system. The proposed system will be evaluated using benchmark performance metrics, and a comparative study will be made with other models. The need to consider the LSTM neural network using acoustic data and linguistic dialect is considered. LSTM networks could produce reliable speaker segmentation outputs.
Unveiling the Potential: Machine Learning and Image Processing for Early Disease Detection in Tulsi Herb
Authors: Manjot Kaur
Published: May 2024

Tulsi (Ocimum tenuiflorum) herb is very much predisposed to infections that influence the growth of the plant and impact the farmer’s ability to learn about the environmental factors affecting the plant. To discover any type of plant infection at a very preliminary stage, a prediction model employing machine learning and image processing techniques can be developed to accelerate the method of disease detection and classification with high-performance metrics. The deployment of various variable preprocessing methods and distinctive factors in the feature extraction process appeared to enhance the implementation of infection recognition and categorization. This article intends to assess and explore the application and implementation of numerous approaches and developments regarding leaf infection categorization and classification. A thorough analysis is provided for disease infection and classification implementation upon examination of formerly recommended avant-garde methods. Finally, challenges and some commendations in this space are considered for the real-time implementation of numerous image computational algorithms for disease detection and recognition of Ocimum tenuiflorum.
Application of Improved Sparrow Search Algorithm to Flexible Job Shop Scheduling Problem
Authors: Long-Yan Xu
Published: May 2024

When the reality of energy saving and reduction of enterprises’ emissions are a concern, research on the investigation of establishing mathematical models to optimize the production time has been studied. In this article, an enhanced sparrow optimization method is proposed. First, two-layer coding is employed for workpieces and machines according to the model requirements. Secondly, the three-dimensional chaotic mapping scheme is presented to improve the population heterogeneity of the algorithm, and the adaptive inertia weight balance algorithm is implemented to offset the speed of the convergence and its probability. Finally, the Cauchy mutation scheme is adopted to help the algorithm jump out of the local optimum. Simulated data is run to check the superiority of the proposed method. So, through the simulations and comparisons of 10 kinds of test datasets, the outcomes suggest that the solution quality of the enhanced sparrow optimization method has been effectively advanced, and its good global optimization ability is shown, which can provide scheduling strategies for workshop productions. One of the successes of the ISSA algorithm is its superior search accuracy.
Performance Scrutiny of Price Prediction on Blockchain Technology Using Machine Learning
Authors: Jayanta Aich
Published: May 2024

Objective: This study, aiming to assess the accurateness of machine learning algorithms for the prediction of cryptocurrency prices, using blockchain technology, was conducted to attest to its effectiveness. The main goal is to evaluate the precision, solidity, and prospects of such models under variable and fragmented trading premises.
Methodology: Dataset containing historical crypto prices, transaction volumes, emotions of investors, etc. is formed from different and various sources. Different machine learning approaches including linear regression, support vector machine, random forest, and deep learning networks will be used to construct the prediction model. The split of the dataset into training and testing subsets is performed to better evaluate and estimate the models' performance. Feature engineering techniques and parameter tuning are included in the process of making models more precise. Models are the ones which are the trained using historical data and then later tested out on the unseen data for the purpose of accurate results and generalization power. Results and Discussion: From the experiment it is evident that a shift of different machine learning algorithms exhibits differing outcomes. While certain models can yield admirable results in forecasting crypto prices in shorter terms, however, there are some models that are not capable of capturing the complicated impurities. Factors like data quality, function selection and model complexity are at the core of predictive power. The learning networks demonstrated the capability to reproduce nonlinearity and temporal relatedness in cryptocurrency value data.
Conclusion: Cryptocurrency forecasting through blockchain technology involves the use of machine learning techniques which has a lot of potential. The success of these models also depends on the quality of the data, feature refinement and model choice. Research progress should focus on refining currently existing models, considering new data inputs, and introduction of advanced machine learning methods which should be able to increase forecast accuracy in rapidly changing cryptocurrency markets.
Algorithmically Generated Domain Names Detection Using Gated Recurrent Unit Deep Learning
Authors: Ranjana B. Nadagoudar
Published: May 2024

The modern malware increasingly employs domain generation algorithms (DGAs) to evade traditional DNS query detection methods, such as blacklisting or reverse engineering of suspicious domain names. These algorithms generate vast numbers of random domain names to establish communication with Command and Control (C&C) servers, posing significant challenges for detection. Previous research has predominantly relied on classical machine learning algorithms, necessitating manual feature extraction and classification, which is both time-consuming and labour-intensive this paper, we propose a deep learning-based architecture for detecting DGA-generated domain names. Our model utilizes recurrent networks with gated recurrent units (GRUs) for domain name detection. By converting domain names into vectors and employing GRUs, the model autonomously learns features, eliminating the need for manual intervention in feature extraction. Compared to traditional methods, our approach reduces time costs associated with feature extraction. The experimental result demonstrates the effectiveness of our proposed GRU achieving 98% accuracy, 94% recall rate, 93% precision, and an Area Under the Curve (AUC) of 99.6%. The GRUarchitecture outperforms LSTM models in terms of recall rate and accuracy while requiring less computational resources, indicating significant performance enhancement.
Energy Needs and Sustainable Development of the World Heritage Site: A Proposition in Lumbini
Authors: Saroj Bhattarai
Published: May 2024

The energy need of people is growing worldwide and the same appears in Nepal. The energy sources apart from petroleum products are hydropower which is in majority and solar are also scattered for household consumption but the use of wind power from traditional methods is evident in the country which is distant from the use of turbines to harness the power of the wind. Focusing on heritage sites with potential such as Lumbini which is a world heritage site faces a lot of problems with energy need as the supply of hydropower and solar are not sufficient enough to fulfill the energy demand. Though the flow of wind is not consistent and high the alternative solution of a low speed wind turbine seems feasible through the preliminary study of wind pressure around the place as the average wind of the place around the year is 4m/s. The speed wind as low as 1.8 m/s could be harvested (Trongtorkarn, M.et.al 2017). The energy need and its sustainable attainability has implications on the involvement of the people for the wellbeing of heritage sites which can be a symbiotic relationship though it has to go through challenges. To reduce the energy demand in heritage areas, in this paper an hybrid solar-wind and hydro-oxygen model is developed. The proposed model is performed using thermodynamic analyses techniques. The result of the model shows, the proposed hybrid approach improves the energy sustainability in heritage areas.
TINJAUAN VALIDASI DATA DI LAPANGAN TENTANG STANDAR PENGABDIAN PADA MASYARAKAT: PADA PRODI PGSD, UNIVERSITAS NAHDLATUL ULAMA AL GHAZALI CILACAP
Authors: Dian Permana
Published: May 2024

Tinjauan Validasi Data Di Lapangan Tentang Standar Pengabdian Pada Masyarakat: Pada Prodi Pendidikan Guru Sekolah Dasar (PGSD), Universitas Nahdlatul Ulama Al Ghazali Cilacap. Adapun dalam tulisan ini penulis mencoba mencari data terkait standar pengabdian kepada masyarakat yang meliputi (standar hasil pengabdian kepada masyarakat, standar isi pengabdian kepada masyarakat, standar proses pengabdian kepada masyarakat) diprogram studi PGSD, Universitas Nahdlatul Ulama Al Ghazali Cilacap dan memvalidasinya, selanjutnya kita akan sinkronkan dengan standar pengabdian kepada masyarakat dari peraturan Kemendikbud RI. Proses selanjutnya, kita akan mengaanalis standar tersebut sampai dengan kita mengetahui hasil-hasilnya. Adapun hasil dari pembahasan ini ada dua katagori. Pertama (Singkron/cocok) dan Kedua (tidak singkron/tidak cokok). Jika hasilnya tidak cocok, maka kita akan mencari atau melacak penyebab dari ketidak cocokan tersebut. Dan terakhir kita akan menyimpulkan pembahasannya. Hasil dari validasi data tersesut pada setiap Standar yakni Standar hasil, Standar Isi dan Standar Proses pengabdian kepada masyarkat pada peraturan kemendikbud No 03 tahun 2020 dengan pelaksanaan pengabdian keapada Masyarakat pada Program Studi PGSD Universitas Nahdlatul Ulama Al Ghazali Cilacap adalah singkron. Walaupun dalam kesingkronan ini tidak semua item yang ada pada standar tersebut, melaikan memenuhi salah satu item didalamnya.
Perintisan Gereja Melalui Kontektualisasi Pemberitaan Injil Pada Suku Nias
Authors: Sirait Junio Richson , Harefa Kristin , Fau Astria Gempita
Published: May 2024

Penelitian ini bertujuan untuk menganalisis perintisan Gereja melalui kontektualisasi pemberitaan Injil pada suku Nias. Suku Nias adalah kelompok etnis yang memiliki kekayaan budaya dan tradisi yang unik di Indonesia. Pada konteks ini, Gereja dan misionaris dalam menghadapi tantangan pemberitaan Injil secara relevan dan efektif kepada suku Nias. Penelitian ini menggunakan pendekatan kualitatif dengan observasi partisipatif, dan analisis konten pemberitaan Injil yang telah diadopsi dalam konteks suku Nias. Hasil penelitian menemukan bahwa Kontektualisasi dilakukan dengan menggabungkan elemen-elemen kebudayaan suku Nias dalam pemberitaan Injil, seperti menggunakan simbol-simbol lokal, bahasa yang akrab, dan memperhatikan konteks adat istiadat. Hal ini memungkinkan pesan Injil untuk diterima secara lebih baik oleh suku Nias dan meningkatkan efektivitas komunikasi antara gereja dan masyarakat. Penelitian ini memberikan kontribusi besar dalam pemahaman tentang perintisan Gereja melalui kontektualisasi pemberitaan Injil pada suku Nias. Temuan ini dapat digunakan sebagai pedoman bagi Gereja dan misionaris yang bekerja di suku Nias untuk mengembangkan strategi pemberitaan yang lebih kontekstual dan relevan.
TINJAUAN TEOLOGIS TERHADAP RESESI SEKS
Authors: Ndraha Tedi Putra Damai , Ndraha Amoli , Waruwu Anwar Three Millenium , Tanto Wignyo , Kusumo Yusuf Setiawan Sudarso
Published: May 2024

Abstrak
Dunia saat ini dihadapkan pada fenomena yang memprihatinkan, yaitu resesi seks, yang mencerminkan penurunan aktivitas seksual di berbagai negara termasuk Singapura dan Jepang. Indonesia juga mengalami tanda-tanda resesi seks dengan lonjakan gerakan menunda pernikahan atau Waithood. Penurunan tingkat kelahiran berpotensi mengancam keberlangsungan manusia dan berdampak pada aspek ekonomi. Studi ini bertujuan memberikan tinjauan teologis terhadap resesi seks dengan fokus pada definisi, penyebab, dampak, dan pandangan Alkitab terhadap seks. Metode penelitian kualitatif deskriptif analitik dilakukan melalui studi pustaka, dengan tahap identifikasi fenomena resesi seks, analisis faktor penyebab, dan eksplorasi pandangan Alkitab. Hasil penelitian menunjukkan kompleksitas resesi seks dengan dampak signifikan pada nilai-nilai moral, hubungan interpersonal, dan pemahaman akan tujuan penciptaan manusia. Tinjauan teologis menyoroti pentingnya seks dalam konteks spiritual dan kebijaksanaan dalam praktik seks yang aman. Studi ini memberikan kontribusi berarti terhadap pemahaman dan refleksi terhadap aspek teologis dari resesi seks, dengan harapan dapat memperkuat hubungan dengan Tuhan dan sesama serta mengatasi dampak negatifnya. 
 
Kata kunci: resesi seks, tinjauan teologis, dampak sosial, pandangan Alkitab, praktik seks yang aman.
THE ETHNOMUSICOLOGY OF THE ILLE LE SONGS, MELOLO COMMUNITY, EAST SUMBA DISTRICT, EAST NUSA TENGGARA
Authors: Iswanto Iswanto
Published: May 2024

Ethnomusicology is concerned with music and society. This research was conducted in Melolo, Umalulu District, East Sumba Regency. The problem in this study is how the ethnomusicology of the singing of Ille Le Melolo, East Sumba Regency, East Nusa Tenggara. The results of the research, the song Ille Le or the sleep songs for boys 'umbu', has unique characteristics in terms of musical form, poetry, and cultural meaning. Musically, this song consists of four notes, namely B, D#, E, and F. The intervals used are dominated by P1, M2, M3, P4, m2, M7, P5. An interval scheme like this will result in static movement and stepping with occasional interval jumps. The meter used is 68. When combined with the existing intervals, it will form a story song. The two lyrics of the song are very distinctive, this is related to the word 'umbu'. The word umbu is always in the lower notation in the musical structure of Ille Le's Song which is 185 Hz (F#3) and 240 Hz (B3). In addition, this song expresses the cultural meaning of the patriarchal system in East Sumba society. Musical structure cannot be separated from poetry. Ille le's ethnomusicological studies link the analysis of musical forms and poetry. Ethnomusicology includes musical characteristics such as the use of pentatonic scales, rhythm, motive, phrase, period and text interpretation.
Online tools helped direct market farmers during the COVID-19 pandemic, but resources are needed for equitable adoption
Authors: Pesci Sasha , Durant Jennie , Manser Gwyneth , Galt Ryan , Asprooth Lauren , Pinzon Natalia
Published: May 2024

This paper examines the use by California’s direct market farmers (DMFs) of online sales and market­ing during the early onset of the COVID-19 pan­demic in the United States, from March through December 2020. The pandemic caused market dis­ruptions that accelerated the trend toward market digitalization. This paper reports quantitative find­ings based on 364 responses to an online survey of DMFs in California and qualitative findings from participant observation and 33 semi-structured interviews with DMFs and technical assistance providers. We found that online sales and market­ing tools, such as social media and websites, were important for withstanding economic disruptions associated with the pandemic, and farmers who had an online presence were more likely to increase their sales and profitability during its early onset. However, we also found that many farmers lacked the necessary resources to access these tools and use them effectively, and that technical assistance providers experienced challenges in helping farm­ers with online technology use. We argue that DMFs need reliable access to the internet, as well as advice, resources, and training to access and benefit from online sales and marketing tools. These resources must be available in languages other than English (e.g., Spanish). Research-informed programs and policies can help DMFs navigate market digitalization and strengthen their resilience to future economic disruptions.
The dynamics of the role of women workers abroad to improve the family economy
Authors: Audia Nanda , Ningsih Nur Wahyu , Kurniawan Arifa , Pramasha Raizky Rienaldy
Published: May 2024

Family economics is a branch of microeconomics that focuses on managing resources to ensure an individual's or small-scale family's survival. It examines how a family's economic status influences their consumption level. It is assumed that as the economic level of a family improves, their expenditures also increase. As household income increases, families tend to purchase more goods and may adopt a more materialistic lifestyle, seeking higher-quality products. This study aims to explore the economic conditions of families with female overseas laborers (TKWs) and understand how their participation in work abroad affects their family's income. The qualitative research method employed in this study utilizes a phenomenological approach, which involves structured interviews, participant observation, and documentation. Primary and secondary data sources are used to collect data. The research findings suggest that the involvement of TKWs has been beneficial in enhancing the family economy. The condition is evident from comparing income levels before and after individuals become TKWs. Women's primary motivation for overseas work is to boost their income.
Analisis sektor unggulan dan perubahan struktur ekonomi pada Provinsi Kepulauan Bangka Belitung sebagai provinsi hasil pemekaran
Authors: Alviansyah Vito Jorghi , Nurhayati Siti Fatimah
Published: May 2024

The research analyzes regional superior sectors and economic changes in the Bangka Belitung Islands Province due to the expansion. Esteban Marquillas' Shift Share analysis was used in research using secondary data from the Central Statistics Agency in the Bangka Belitung Islands Province. This research analysis shows that the processing industry, forestry, and fisheries sectors are the regional primary sectors among the 17 business sectors in the Bangka Belitung Islands Province. Then, changes in the economic structure throughout 2020–2022 are said to be a transition period. Therefore, most local people are still involved in the secondary and primary sectors, compared to the tertiary and service sectors.
Pemanfaatan Internet of Things (IOT) untuk Monitoring Tumbuh Kembang Tanaman Anggrek
Authors: Sulistyo Gunawan Budi , Safitri Lina Ayu , Kiswati Sri , Adjie Bayu Kresna , Fauzi Muhammad Naufal Zul
Published: May 2024

Kelompok Wanita Tani (KWT) menjadi  media pemberdayaan masyarakat khususnya para wanita agar dapat memanfaatkan lahan yang dimiliki agar menjadi bermanfaat dan produktif. Mereka diharapkan dapat membuka dan menciptakan ruang terbuka hijau untuk membudidayakan tanaman baik sumber pangan maupun budidaya tanaman hias salah satunya tanaman anggrek. Semakin berkembangnya teknologi memudahkan dalam memantau tumbuh kembang tanaman, Salah satunya dengan pemanfaatan Internet of Things (IOT). Penggunaan teknologi IOT ( Internet of Things) dilakukan dengan memanfaatkan sensor dari perangkat elektronik yang terhubungkan untuk memantau tumbuh kembang tanaman secara otomatis. Dengan penggunaan system ini diharapkan para pelaku budidaya tanaman hias mendapatkan banyak manfaat sepeerti adanya efisiensi pemakaian air dan hemat waktu.
Upaya penanaman karakter rajin pada anak usia dini
Authors: Wibisono Audi , Sugianto Everson , Velicia Gela Kathryn , Linawati Nanik
Published: May 2024

For four days, community service activities were given to early childhood children at Rainbow Kiddy Kindergarten, Surabaya. Eight children participated, and all of them were kindergarten B students. The aim of holding this activity was to provide understanding and instill habits for productive and diligent living. The learning method is done through puppet stages that present educational stories, block assembly games, drawing on scratch paper, puzzle games, and coloring fans and piggy banks. The results of this community service activity show that the socialization activity has made children understand the importance of being diligent. Puzzle games and assembling blocks train children's motoric, sensory, and mental cognitive abilities to make efforts. Drawing activities on scratch paper and coloring fans encourage children to be more expressive and creative. Coloring piggy banks encourages creativity and the intention to start saving.
An extended Debt Sustainability Analysis framework for Latin American economies
Authors: Kataryniuk Iván , Lorenzo Alonso Raquel , Martínez Casillas Enrique , Timini Jacopo
Published: May 2024

The COVID-19 pandemic marked a watershed for public finances in Latin America and around the world. Fiscal measures adopted in 2020 to cope with the health emergency were substantial and affected debt dynamics. While the situation partially reverted in the following years, public debt is still higher than its recent historical average for most countries in the region. In this context, the sustainability of public debt dynamics has taken on renewed importance. In this paper, we extend a standard Debt Sustainability Analysis (DSA) framework that considers significant features of Latin American economies – such as the existence of foreign currency denominated debt – by introducing an economic model that jointly determines future values of key macroeconomic variables. We then compute different scenarios for Brazil, Chile, Colombia, Mexico, and Peru, illustrating how fiscal and structural policy changes affect the dynamics of public debt.
Masquerading parasites: A case study on pulmonary echinococcosis mimicking lung cancer and bronchogenic cyst
Authors: Abulhaija Mustafa , Jacobs Derek , Dominguez Dallas , Abulhaija Ashraf , Wesolow Jacqueline , N Greene John
Published: May 2024

Introduction: Pulmonary echinococcosis, caused by the larval stage of Echinococcus granuloses, presents significant diagnostic challenges, often mimicking more common thoracic pathologies, such as lung cancer and bronchogenic cysts. This report underscores the importance of considering echinococcosis in differential diagnoses for lung lesions.

Case Report: We discuss the case of a 24-year-old male, initially suspected to have an intrapulmonary bronchogenic cyst, later diagnosed with pulmonary echinococcosis. The diagnosis was established through a combination of radiological findings, surgical intervention, and pathological examination, highlighting the complexities involved in correctly identifying this parasitic infection.

Conclusion: This case illustrates the critical need for heightened awareness and a comprehensive diagnostic approach to pulmonary echinococcosis, particularly in endemic areas. It emphasizes the role of detailed imaging and consideration of echinococcosis in the differential diagnosis of cystic lung lesions to prevent misdiagnosis and ensure appropriate treatment.
Estimación de la eficiencia productiva en la extracción de aceite esencial a partir de la cáscara de la naranja mediante redes neuronales
Authors: Fajardo Muñoz Sandra Elvira , Freire Castro Anthony Josue , Mejía Garzón Michael Isaac
Published: May 2024

En este trabajo, se utilizó una Red Neuronal Artificial (RNA) feedforward con 9 capas ocultas y algoritmos de entrenamiento backpropagation (BP) y de ajuste de pesos Levenberg- Marquardt (LM) para la predicción del rendimiento de extracción de aceite a partir de la cáscara de naranja (Citrus sinensis), para el entrenamiento y validación, se emplearon los datos en cantidad de carga en gramos como variable de entrada y el rendimiento de aceite en porcentaje como variable de salida, los cuales se obtuvieron en la técnica de destilación por arrastre de vapor usando la trampa Clevenger. Se estudiaron distintas arquitecturas variando el número de neuronas en la capa oculta, encontrando que la RNA con 9 neuronas brindaba el mejor ajuste de los datos experimentales, lo que indica mayor eficacia y exactitud frente a las otras arquitecturas analizadas. Con respecto a los datos experimentales, se evaluó el error cuadrado medio porcentual (ECM%) y el coeficiente de determinación , encontrándose para la RNA valores de ECM%=0.0040 y =0,9929, comprobando que la hipótesis de investigación es verdadera. Estos resultados muestran la eficacia y potencialidad del uso de las redes neuronales para el modelado y predicción del rendimiento de extracción de aceite de naranja dentro del dominio de los datos de entrenamiento.
Dores osteomusculares e qualidade de vida de agricultoras familiares, Belterra, Pará, Brasil
Authors: Wanderley Liliana , Wandeley Jonival Furtado , Leite Iani Dias Lauer , Vieira Thiago Almeida
Published: May 2024

Objetivo: mensurar dores osteomusculares sentidas por agricultoras familiares; apontar a região do corpo mais atingida e verificar a percepção da qualidade de vida do público estudado. Metodologia: Foram aplicados: questionário sociodemográfico; inventário breve da dor e o questionário de qualidade de vida Whoquol-Bref em 14 agricultoras, com idade entre 18 e 55 anos, pertencentes a uma associação de mulheres rurais de Belterra, Pará. Resultados: O perfil populacional apontou mulheres com media etária de 41 anos; com mais de 4 filhos; ensino médio completo; renda de um salário mínimo. Apresentam dor osteomuscular moderada e a região lombar a mais afetada. A qualidade de vida foi regular. Conclusão: As agricultoras familiares apresentaram queixas álgicas, principalmente na região lombar, refletindo na redução da sua percepção de qualidade de vida. Isto pode estar relacionado à má postura, e múltiplas funções desempenhadas por elas, sendo recomendada orientação profissional às mulheres quanto à melhores posturas.