bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive dermascopy application using machine learning
Authors: Maneesha Alavala , Anusha Kondam , Kalyani Bjd
Published: March 2024

Skin cancer is the most lethal because skin cells develop abnormally. Finding skin cancer early is very important and may help stop some kinds of skin cancer, like melanoma and focal cell carcinoma. Early detection and classification of skin cancer are difficult and costly. Recurrent networks and ConvNets can automatically extract complex data. This paper proposes to use a handmade features-based multi-layer perceptron and a cascaded ensembled network to upgrade ConvNet models. This convolutional neural network model detects non-handmade picture qualities and generates features like color moments and material properties. With ensembled DL, accuracy increased from 85.3% with convolutional neural networks to 98.3%.
Sustainability-centric integration software requirements validation
Authors: Kandukuri Prabhakar , Syma Kumar Dasari N.V. , Ramesh G. , Kativarapu Prathima
Published: March 2024

Software Development Life Cycle (SDLC) is enriched with different phases. Requirement Analysis is predominant in designing and developing the software application with proper functionality and to produce expected outcome. The client expects the great deal of functionality and ease of use application and outstanding performance from the application developed. If the application is appropriately demonstrating the outcome what the client expected the requirements have to be gathered once again and re do the application until the client satisfaction. Understanding the client specific requirements from the users’ point of view and smooth running of the operations with proper integration between the modules of the application. The requirements from the client would be vague and taken in the user’s perception. The requirements taken from the client’s user requirements should be integrated with realistic expectations and plan according to the project schedule for each stage of SDLC. The requirement gathering technique should replicate the software requirement engineering with proper integration oriented methods. So that the project development will be completed without facing the challenges and issues arise in the different stages of SDLC. This research is proposing the integration oriented scrutiny on the software requirement engineering mechanism in the requirement analysis phase of SDLC.
A novel time series classification for multivariate data using improved deep belief-recurrent neural network with optimal dynamic time warping
Authors: Mohan Babu Bukya , Sandhya B.
Published: March 2024

In the past ten years, data from time series extraction has attracted a lot of attention. Several methods have concentrated on classification problems, where the objective is to identify the labelling of a test period, given labelled training data. Feature-based and Instance-based methods are the two fundamental groups into which time series categorization methodologies may be divided. To categorize time series data, instance-based techniques use similarity data in a nearest-neighbor context. While methods in this category deliver reliable findings, their efficacy suffers when dealing with lengthy and noisy time series. Feature-based approaches, on the other together, extract characteristics to address the shortcomings of instance-based methods; nevertheless, these approaches use predetermined features and might not be effective in all classification issues. This paper seeks to introduce a novel deep learning-based Optimal Dynamic Time Warping (ODTW) paradigm for multimodal time’s series data categorization. This model covers several phases. At initial stage, the standard data is gathered from standard public source. Secondly, ODTW is proposed, where the parameters are optimized by Random Opposition Billiards-Inspired Optimization (RO-BIO) for extracting the most essential information. Finally, the classification is carried out through “Deep Belief Network (DBN) and Recurrent Neural Networks (RNN) termed as Deep Belief-RNN (DB-RNN)”. Finally, the extracted deep features are given to the optimized RNN for attaining the final classified results. The simulation results have resulted in superior classification performance in terms of standard performance measures.
Classification of waste in natural environments
Authors: Kumar Kanaparthi Suresh , Keerthi Reddy Challa , Sai Rama Sharma Tripurari V. , Aravind Kumar Reddy K. , Sragvi Reddy Nelapatla , Vishnu Athmakuru
Published: March 2024

A worldwide issue affecting the world is waste disposal; hence, the need to construct a waste detection system that is based on deep learning. Ten wastes are evaluated in this article with a view of providing an integrated framework for measurement and evaluation. It also offers a comprehensive account of the wastes studied further by looking at its detail finding and the challenges they pose as well as views associated with waste detection. Recycling both has economical and ecological benefits which help to eliminate pollution. Technology can now be used to turn waste into resource materials thus recycling more than before especially through the use of deep learning among other technologies.
Optimizing wireless charging infrastructure placement using genetic algorithms
Authors: Maksudovna Vafaeva Khristina , Saritha K.
Published: March 2024

Electric vehicles (EVs) play a crucial role in tackling environmental issues in the transportation industry. The incorporation of effective charging infrastructure is crucial in promoting the broad acceptance of electric vehicles (EVs). This work investigates the optimization of the location of wireless charging infrastructure in urban contexts using genetic algorithms (GAs). The location data, which includes latitude and longitude coordinates, showed a wide range of spatial distributions that are ideal for deploying charging stations. These distributions display variances that are favorable for strategically placing the infrastructure. The examination of power consumption data revealed significant variations in energy demand across different sites, ranging from 180 kWh to 300 kWh. These differences indicate that each location has its own distinct energy needs. The population density statistics exhibited a spectrum of values, ranging from 600 individuals per square unit. The population density is 1200 persons per square kilometer. The abbreviation "km" refers to kilometers, which is used to indicate different levels of prospective electric vehicle (EV) users. In addition, the distance data provided information about the lengths between prospective locations for charging stations, which varied from 400 km to 1200 km. These distances had an impact on the concerns of connection and transmission efficiency. The research highlights the intricate nature of the elements that affect the ideal location of infrastructure, underlining the need for a methodical approach to optimization. Integrating these statistics provides a foundation for developing an objective function in the GA framework to optimize the location of charging infrastructure. The study's results provide valuable understanding of the many factors that influence the location of charging infrastructure. The goal is to promote the development of efficient and easily accessible electric vehicle charging networks in metropolitan areas.
Reinforcement learning-based energy storage management in smart grids
Authors: Iurevna Usanova Kseniia , Bharadwaj V.Y.
Published: March 2024

This study investigates the use of reinforcement learning (RL) techniques as a dynamic control mechanism to enhance the management of energy storage in smart grid systems. The research aims to optimize the efficiency of energy storage operations by analyzing collected data from different time intervals in a simulated smart grid scenario. An evaluation of the energy storage status reveals a consistent upward trend in the quantity of stored energy, with a 30% cumulative growth across time intervals. An examination of the demand and supply of the grid indicates a persistent insufficiency of energy, with an average shortfall of 15% in meeting the requirements of the system. Through the use of reinforcement learning (RL) methodologies, the system exhibits a remarkable 450% improvement in cumulative rewards, providing substantiation of its capacity to acquire knowledge and adjust its behavior over time. The system's actions indicate a purposeful shift in strategy, with 75% of instances involving charging procedures, emphasizing a commitment to energy preservation and the buildup of stored energy. Despite a shift in approach, persistent disparities between grid demand and supply need the implementation of more accurate technologies for effective energy management. The findings highlight the effectiveness of using reinforcement learning (RL) for managing energy storage in smart grids. This approach improves energy reserves and optimizes energy storage by altering actions accordingly. These insights contribute to the advancement of adaptive energy management strategies, resulting in the development of sustainable and resilient smart grid infrastructures.
Intelligent agents for advanced power system protection schemes
Authors: Igorevich Rozhdestvenskiy Oleg , Deepthi P.
Published: March 2024

This study explores the incorporation of intelligent agents to improve power system safety, using several computational models including machine learning, rule-based systems, neural networks, and fuzzy logic. The research assesses the effectiveness and efficiency of these agents in promptly identifying, categorizing, and responding to faults in the power system architecture using empirical analysis. The results demonstrate the higher performance of agents based on neural networks, with an average improvement in fault prediction accuracy of 38% compared to systems based on rules. Furthermore, the evaluation of power system devices demonstrates a direct relationship between greater voltage ratings and increased expenses for both installation and maintenance, underscoring their crucial importance within the system. An examination of fault severity reveals that greater severity failures have a direct and significant influence on system downtime. These problems lead to longer interruptions, which emphasizes the need of implementing effective fault management systems. Intelligent agents' actions have different costs and reaction times. Actions based on neural networks have lower average costs and shorter response times, demonstrating their cost-effectiveness and efficiency in addressing faults. The study of percentage change highlights the importance of using various kinds of intelligent agents and higher-rated devices. This research offers insights into performance differences and the consequences for optimizing protection measures. This research provides a thorough understanding of how intelligent agents may enhance power system protection. It also offers guidance for future improvements in creating power grid infrastructures that are robust, dependable, and adaptable.
Real-time Adaptive Control of Electric Vehicle Drives using Artificial Neural Networks
Authors: Vladimirovich Kotov Evgeny , Mallikarjuna Raju K.
Published: March 2024

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.
Enabling Sustainable Urban Transportation with Predictive Analytics and IoT
Authors: Igorevich Rozhdestvenskiy Oleg , Poornima E.
Published: March 2024

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, using empirical data gathered from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its correspondence with actual data. The data reveals a direct relationship between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of the models in forecasting congestion and AQI, thereby confirming their effectiveness. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban transportation, enabling more intelligent decision-making, and creating sustainable urban environments driven by data-driven insights and proactive actions.
Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms
Authors: Mittal Aman , Karuna G.
Published: March 2024

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.
« Les lapin cour vite » ! Ce que révèle la morphographie des apprentis scripteurs sur la morphologie du français
Authors: Surcouf Christian
Published: March 2024

Face aux graphies non-normées dans les chaines d’accords chez les apprentis scripteurs, (psycho)linguistes et didacticiens s’interrogent normalement sur les raisons de ces écarts morphographiques ou sur la manière dont l’enseignement de l’orthographe grammaticale pourrait être amélioré pour garantir un meilleur résultat. Nous prenons pour notre part le contrepied de cette approche en envisageant ces erreurs récurrentes comme révélatrices de l’intuition des locuteurs sur les véritables fonctionnements de la langue en synchronie. Pour des raisons épistémologiques, il parait dès lors fondamental de différencier les fonctionnements de la langue de ceux du système d’écriture. Pourtant, les grammaires linguistiques continuent de décrire la morphologie de la langue vivante contemporaine à l’aune des principes morphographiques, qui reflètent quant à eux des états de langue disparus. Ainsi en écrivant « les lapin cour vite », le jeune francophone encore épargné par le discours grammatical nous révèle qu’en français contemporain le pluriel des noms a disparu et que le verbe courir, à l’instar de tant d’autres, n’a qu’une seule forme pour les 1re, 2e, 3e et 6e personnes.
Évolution des compétences rédactionnelles à l’école primaire : quelques pistes exploratoires à partir de méthodes quantitative et qualitative
Authors: Benzitoun Christophe , Legallois Dominique
Published: March 2024

Dans cet article, nous proposons de recourir à des techniques issues de la linguistique de corpus et de la linguistique de l’oral pour observer l’évolution des compétences rédactionnelles d’élèves francophones. Pour ce faire, nous présentons principalement une méthode non-supervisée basée sur des ngrams ainsi que le dispositif de l’analyse en grille (Blanche-Benveniste et al., 1979). Notre étude porte sur le corpus longitudinal Scoledit (Wolfarth et al., 2017).
Qu’est-ce qui s’affiche en Étude de la langue au cycle 3 ? Ce que les affichages révèlent de leurs concepteurs
Authors: Lavieu-Gwozdz Belinda
Published: March 2024

L’affichage didactique appartient à l’environnement scolaire et participe à la démarche d’apprentissage en offrant un support visuel à l’élève. Notre travail vise à décrire le savoir qui y figure et d’accéder ainsi au rapport à la langue que les enseignants véhiculent dans leur classe. Comment sont construits ces affichages ? Quelles ressources lexico-syntaxiques et discursives sont proposées ? Ces supports explicitent-ils des procédures ? Présentent-ils la régularité du fonctionnement de la langue ou constituent-ils des aides à la mémorisation ? Présentent-ils des règles à savoir par cœur ? Quelles représentations de la langue et de son fonctionnement peut-on (ou non) développer dessus ?

Les premiers résultats indiquent que les affichages sont avant tout des aide-mémoires qui témoignent d’une représentation non systémique de la langue. C’est cette manière de percevoir la langue qui est souvent transmise dans la mesure où le principe classificatoire de la langue est rarement rendu visible.
Annotation de la cohérence dans un corpus de textes d’élèves d’école et collège
Authors: Bras Myriam , Vieu Laure
Published: March 2024

Cet article traite de la question de la cohérence dans des productions écrites d’élèves d’école et collège. Le corpus analysé est issu du corpus RESOLCO, il est constitué de 36 textes d’élèves d’école primaire et de collège (CE2, 6èmeet 3ème) produits selon une même consigne d’écriture, une tâche-problème demandant aux élèves la production d’un texte narratif impliquant la résolution d’anaphores de divers types. Il a été annoté dans le cadre du projet ECalm avec l’objectif d’analyser la cohérence à partir de l’identification de relations entre segments de texte. Le schéma d’annotation est basé sur la Segmented Discourse Representation Theory qui définit de façon formelle ce qu’est une représentation de discours cohérente. L’article présente la méthodologie de segmentation des textes en Unités de Discours Élémentaires et d’annotation en Relations de Discours et en problèmes de cohérence (incohérences locales, impossibilités d’inférer une Relation de Discours, ou impossibilités d’attacher la représentation d’un segment au reste de la représentation), réalisant une extension originale de la SDRT. L’analyse des annotations met en évidence une tendance à la baisse de ces trois types d’indicateurs, traduisant une progression de la cohérence du CE2 à la 3ème, avec un palier significatif entre le CE2 et la 6ème, à corréler avec un saut majeur de compétences rédactionnelles entre la fin du cycle 2 et la fin du cycle 3. La théorie, mise à l’épreuve ici pour la première fois sur des textes d’apprenants, se trouve ainsi validée empiriquement dans sa version étendue.
Structuration progressive du discours narratif chez le jeune enfant non lecteur-scripteur : analyse d’un corpus de productions écrites en dictée à l’adulte
Authors: Tabourdeau Gilles , Vergez-Couret Marianne
Published: March 2024

Cet article propose d’examiner la progression dans la structuration d’un discours narratif chez des élèves de maternelle dans la pratique de la dictée à l’adulte. Une typologie des stades de développement du discours narratif est présentée, allant de phrases juxtaposées sans connecteurs à des discours démontrant une maîtrise des procédés de cohésion et de cohérence. L’étude met en évidence les interactions entre l’enseignant et l’élève, décrivant les différentes postures d’étayage adoptées par l’enseignant et les micro-gestes de négociation discursive utilisés pour guider l’élève. Ces interactions jouent un rôle dans le développement des compétences de l’élève. Cette recherche exploratoire propose de croiser l’approche linguistique de la cohérence et de la cohésion des discours et une réflexion didactique autour du rôle de l’étayage et des stratégies d’intervention des enseignant-es.
Les interventions des enseignants débutants dans les copies des étudiants en droit
Authors: Scheepers Caroline
Published: March 2024

Ont été analysées au moyen d’un grand nombre d’indicateurs les annotations présentes dans plusieurs centaines de résumés ou de synthèses élaborés par des primoétudiants inscrits dans un bachelier en Droit. Je me suis centrée sur les pratiques évaluatives de quatre assistants, à qui ont été confiés des séminaires en sciences humaines (criminologie, sociologie, histoire contemporaine). Les analyses montrent des résultats assez divergents par rapport aux études existantes en la matière.
Global horizontal irradiance prediction for renewable energy system in Najran and Riyadh
Authors: Irfan Muhammad , Shaf Ahmad , Ali Tariq , Zafar Maryam , AlThobiani Faisal , Almas Majid A. , Attar H. M. , Alqhatani Abdulmajeed , Rahman Saifur , Almawgani Abdulkarem H. M.
Published: March 2024

Producing and supplying energy efficiently are important for many countries. Using models to predict energy production can help reduce costs, improve efficiency, and make energy systems work better. This research predicts solar electricity production in the Najran and Riyadh regions of Saudi Arabia by analyzing 14 weather factors. The weather factors that were considered in the study include date, time, Global Horizontal Irradiance (GHI), clear sky, top of atmosphere, code, temperature, relative humidity, pressure, wind speed, wind direction, rainfall, snowfall, and snow depth. GHI is the most important factor because it determines how much solar energy a system can produce. Therefore, it is important to be able to predict GHI accurately. This study used a variety of data-driven models to predict GHI, including the elastic net regression, linear regression, random forest, k-nearest neighbor, gradient boosting regressor, light gradient boosting regressor, extreme gradient boosting regressor, and decision tree regressor. The models were evaluated using a set of metrics, including the mean absolute error, mean squared error, root mean square error, coefficient of determination (R2), and adjusted coefficient of determination. This study found that the decision tree regression, Random Forest (RF), and Extreme Gradient Boosting (XGB) models performed better in the Riyadh region than in the Najran region. The R2 values for the Riyadh region were 99%, 99%, and 98%, while the R2 values for the Najran region were 89%, 94%, and 94%. This suggests that the Riyadh region is a more suitable location for solar energy conversion systems. These findings are important for policymakers and investors who are considering the development of solar energy projects in Saudi Arabia.
An investigation into cut surface quality of Al6061/Al2O3/B4C composite using hydro-abrasive machining process
Authors: Muthulakshmanan A. , Adalarasan R. , Santhanakumar M. , Gnanavelbabu A. , Makki Emad , Giri Jayant
Published: March 2024

The modern era has realized the tremendous potential of metal matrix composites (MMCs) in slowly substituting the traditional materials used in automotive and aerospace industries. Traditional machining of second generation MMCs is quite challenging due to heat generation, tool wear, and low cut surface quality. This work presents an analysis on hydro-abrasive machining (HAM) of the Al6061/Al2O3/B4C composite by using an amalgamated method of gray incidence based response surfaces (GIRS) to produce a good surface quality. The influence of HAM variables [abrasive flow rate (FA), waterjet pressure (PWJ), jet traverse speed (SJT), and stand-off distance (DS)] on various cut surface characteristics [surface roughness (Ra), striation-free zone (SFZ), striation angle (ΘS), and kerf angle (ΘK)] is examined using L27 OA and response surface plots. A hybrid approach of the GIRS method is presented for necessary polynomial modeling and simultaneous optimization. The reduction of response values is primarily influenced by the waterjet pressure and jet traverse speed. An optimal cutting condition resulted in a significant reduction in surface roughness by 19.22% and striation angle by 10.82%. The improvements in striation-free zone and kerf angle were also notable with reductions of 7.89% and 4.74%, respectively. FESEM images of machined surfaces, profile graphs of surface roughness, and images observed using atomic force microscopy were also studied.
Epitaxial growth of high-quality GaAs on Si(001) using ultrathin buffer layers
Authors: Cheng Kun , Tang Tianyi , Zhan Wenkang , Sun Zhenyu , Xu Bo , Zhao Chao , Wang Zhanguo
Published: March 2024

The direct growth of III–V semiconductors on silicon holds tremendous potential for photonics applications. However, the inherent differences in their properties lead to defects in the epitaxial layer, including threading dislocations (TDs), antiphase boundaries (APBs), and thermal cracks, significantly impacting device performance. Current processes struggle to suppress these defects simultaneously, necessitating the development of methods to inhibit TDs and APBs in a thin buffer on silicon. This study introduces a GaSb buffer layer during GaAs epitaxy on a silicon (001) substrate. This approach successfully suppresses defect formation by promoting the formation of interfacial misfit dislocation arrays at both the AlSb/Si and GaAs/GaSb interfaces. The resulting GaAs layer exhibits a step-flow surface with a rough mean square of ∼3.8 nm and a full width at half maximum of 158 arcsec. Remarkably, the growth is achieved without any observable interfacial intermixing. Building on this platform, InAs/GaAs quantum dots are grown with a density of 3.8 × 1010 cm−2, emitting at a wavelength of 1288 nm. This breakthrough holds immense promise for developing high-quality GaAs films with reduced defect densities on silicon for O band lasers, laying the foundation for the mass production of silicon-based integrated circuits.
Modeling the mechanical response of microelectromechanical system (MEMS)-based sensors to volatile alcohol vapors: A finite element analysis
Authors: Alrashed Haya , Obeid Abdulfattah , Albrithen Hamad , Muthuramamoorthy Muthumareeswaran , Rahman Shofiur , Al-Gawati Mahmoud A. , Alodhayb Abdullah N.
Published: March 2024

The development of microelectromechanical system-based sensors, such as microcantilever sensors, has garnered considerable interest across various fields. Notably, there is a significant focus on the detection of volatile alcohol vapors, which holds promise in mitigating breath-related illnesses. This study employed finite element analysis to simulate the deflection of a silica-based microcantilever coated with polymethyl methacrylate in response to different volatile alcohol vapors (VAVs), such as methanol, ethanol, and isopropanol. A Multiphysics framework was used to analyze the time-dependent response of a microcantilever to different concentrations of VAVs incorporating fluid–structure interaction. This complex model integrated the aspects of laminar flow, solid mechanics, and transport of dilute species. Significant agreement has been achieved between finite element analysis-simulated results and the experimental findings that we had previously documented. This alignment revealed consistent trends, with methanol exhibiting higher levels than ethanol, followed by isopropanol, further validating the robustness and reliability of the sensor system in VAV detection.
Analysis of DC bias characteristics of transformer by using fixed-point time-step FEM and dynamic J–A hysteresis model
Authors: Wang Yaqi , Li Lin , Zhao Xiaojun , Sun Jia’an
Published: March 2024

The injection of the Direct Current (DC) bias component will lead to oversaturation of the transformer core and affect its safe operation. In this paper, the vector magnetic potential A and winding current I are used as the variables solved in the time-step finite element method to simulate the nonlinear transient magnetization characteristics of the transformer under DC bias, and the fixed-point reluctivity is introduced to deal with the nonlinear iterative problem. The field-circuit coupling relationship is established through the electromagnetic induction law, and the two-dimensional time-step finite element model of the transformer core is found. The dynamic J–A hysteresis model is used to deal with the hysteresis effect of iron cores. The fixed-point reluctivity of difference form is introduced to solve the problem of uneven transition and multi-value of magnetization curve with hysteresis in the iterative process. The determination scheme for the equivalent thickness of the core model and the initial iterative value are discussed. A laminated core transformer is made for experiments, and the accuracy and applicability of the model proposed in this paper are verified by comparing the calculation and experimental results. The influence of different voltages and different DC bias components on the electromagnetic characteristics of the transformer is analyzed.
On mid-Atlantic Spanish in literary translation
Authors: Cascales Serrano Miguel Ángel
Published: March 2024

This article applies the mid-Atlantic English approach described by Michael Henry Heim (2014) to the context of Spanish in literary translation. After describing Heim’s approach, it seeks to
establish common points between this approach and a mid-Atlantic variant of Spanish. This is followed by a discussion of the
background and attitudes towards neutral Spanish, and the potential advantages of a mid-Atlantic variant for the translation of
dialectal language. Finally, it proposes a list of features from different Spanish variants to create potential geographically
neutral variants of Spanish, in line with Heim’s features for his regional mid-Atlantic English.
X(3960), X
0(4140), and other compact
states*
Authors: Li 李 Shi-Yuan 世渊 , Liu 刘 Yan-Rui 言锐 , Man 满 Zi-Long 自龙 , Si 司 Zong-Guo 宗国 , Wu 武 Jing 敬
Published: March 2024

We studied the spectrum and rearrangement decays of S-wave

tetraquark states in a simplified quark model. The masses and widths were estimated by assuming that

is the lower

tetraquark. Comparing our results with experimental measurements, we found that

, recently observed by LHCb, can be considered the lowest

tetraquark state and

could be the second lowest

tetraquark. Predictions of ratios between partial widths for the involved tetraquarks are provided in this paper. We aim to identify more

tetraquarks with

,

, and

.
Estimation of Carbon Stock Due to Land Cover Change in Small Islands: A Case of Gili Matra Islands, Indonesia’s Marine Tourism Park
Authors: Ita Selvia Siska , Arifin Aria Bakti Lalu , Sukartono  , Suwardji  , Hari Kusumo Bambang
Published: March 2024

As a small island cluster, Gili Matra (Meno, Trawangan, and Air) is the main tourist attraction in West Nusa Tenggara. The trend of population growth in Gili Matra, as well as the trend of tourist visits, continues to increase. It needs to be more balanced between the demand for living space and developing tourism-supporting facilities. As a result, land cover changes are unavoidable, significantly reducing the non-built-up areas. Land cover changes have an impact on ecosystem and climate control functions. The signs of climate change in small islands, such as extreme weather, sea level rise, and coastal abrasion, are starting to be felt. The purpose of this study is to calculate the conversion of carbon stocks based on land cover changes in Gili Matra from 2013 to 2022. The analytical method used to land cover change and analyse carbon stocks. The research showed that the built-up areas increased from 126.84 hectares to 223.55 hectares from 2013 to 2022. Meanwhile, non-built-up areas such as sand beaches, plantations, bare areas, shrubs, and mangrove areas decreased from 576.56 to 479.84 hectares. In addition, mangroves, plantations, and shrubs, three examples of land cover with the highest carbon conversion, have decreased by 28.67%, 11.25%, and 6.36%, respectively. The total carbon stock on Gili Matra in the 2013–2022 period has decreased by 1,802.08 tons C. It indicates increased carbon emissions due to land cover changes in Gili Matra caused by increased demands on space for settlements and tourism support facilities.
Measuring Community Acceptance on Waste Separation in Rural Informal Waste Sector
Authors: Meidiana Christia , Thi Trần Nguyệt , Nyoman Suluh Wijaya I , Amalia Rizky
Published: March 2024

Indonesia’s waste management target is 100% access to waste service by 2025 comprising 70% waste handling and 30% waste reduction. Target achievement requires support from all stakeholders in waste management including the community as stated in National waste law 18/2008. Community involvement in waste management is accommodated through the informal waste sector, including waste bank national wide. In this study, the community involvement in waste separation and reduction through WB was measured using willingness to accept (WTA). WTA as a tool of contingent valuation method was used using questionnaires containing some dichotomous questions to get the data for the analysis to find out the responses of the community for the responsibility to separate the waste. Questionnaires were distributed among WB’s members as respondents to identify their willingness for waste separation and sale. Previously, WB efficiency was calculated using elasticity index whose variables are income and the active members. The acceptable price for recyclable material sold to WB was assessed afterward. The result shows that out of five WBs, three WBs are efficient, and these three WBs have stable active members generating stable income which is a determinant for WB efficiency. The regression community’s acceptance is 92.5% indicating that most people have a positive response to WB, but they expect a higher selling price for the recyclable waste. Moreover, acceptable prices and sufficient service for WB members are the factors that can stimulate the community to involve in waste separation and sale while supporting the sustainable waste management.
Evaluation Performance of Service Facilities at Ferry Port Bajoe Village, Southeast Sulawesi
Authors: Hariyani Septiana , Ode Safina Tunnaja Wa
Published: March 2024

The Bajoe Village Ferry Port was a port that specifically served the movement of tourists on Bokori Island, which had been operating for eight years. The number of tourists using this port service reached 40,194 passengers in 2019; however, services at the port still had shortcomings. The aim of this research was to evaluate port service facilities based on passenger perceptions so that the service performance of the Bajoe Village Ferry Port remained optimal in serving tourists. The methods used were comparative descriptive analysis, IPA analysis, and QFD analysis to produce technical responses to priority service attributes based on tourist desires. The final result of this research was in the form of directions for developing Bajoe Village Ferry Port service facilities, consisting of toilets, accessibility, lighting, and information service facilities.
Analysis of carrageenan quality of Eucheuma cottonii and Eucheuma spinosum seaweed from Bantaeng Regency and its export permit regulations in Indonesia
Authors: Lestari Mega Fia , Yusra Sri , Fuady Muhammad Ikram Nur , Rahim Herlina
Published: March 2024

One of the valuable compounds contained in seaweed is carrageenan, which is found in Eucheuma cottonii and Eucheuma spinosum, the most common species found in Bantaeng Regency, Indonesia. This study aims to measure the quality of carrageenan in those Eucheuma and elaborate on seaweed export regulations in Indonesia. The method used is KCl precipitation, where analysis is carried out on the yield value, moisture and ash content, and pH of carrageenan with reference to the Food and Agriculture Organization (FAO) standards. While the rules for export permits will be explained qualitatively using a statutory approach. The findings showed that the amount of moisture and ash content that met FAO standards was found in Eucheuma cottonii, and it also had the highest yield value. Furthermore, in terms of export permits, exporters need a certificate of origin for seaweed, which is regulated in the Regulation of the Minister of Maritime Affairs and Fisheries No. 7/PERMEN-KP/2013, which aims to provide legal certainty regarding product quality for both exporters and importers. This study recommends that it is required to vary the concentration of the KOH solution used to soak the seaweed in order to obtain a pH of carrageenan that meets FAO standards.
Effect of climate change to characteristic of extreme rainfall over Batanghari watershed
Authors: Handoko Unggul , Boer Rizaldi , Aldrian Edvin , Misnawati  , Dasanto Bambang Dwi , Apip
Published: March 2024

Climate change will cause extreme rainfall, both wet and dry. Extreme rainfall will cause catastrophic floods and droughts. Therefore, it is important to analyse extreme rainfall and flood events in the future. Disasters and environmental issues in the Batanghari watershed include flooding, drought, forest and land fires, sedimentation, and water quality degradation. Therefore, the Batanghari watershed is selected as a study area in this research. Research on climate change in the Batanghari watershed is crucial to understanding the trend of extreme conditions now and in the future. Characteristics of extreme rainfall will be analysed using the climate index that was developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Results show that climate change in the Batanghari watershed is indicated by increasing 5 of 6 extreme rainfall indices: R95p, R99p, Rx1day, Rx5day, and SDII. An increase in extreme rainfall is correlated with an increase in flood events in the Batanghari watershed in similar periods. The extreme rainfall index that most influences flooding in the Batanghari watershed is PRCPTOT and R99p. Flooding is expected to occur more frequently in the future than it does now. The Batanghari Hilir Sub-watershed is more vulnerable to extreme flooding in the future than other sub-watershed.
Knowledge of disaster safe education at landslide-prone schools in Tawangmangu, Indonesia
Authors: Ronggowulan Lintang , Nugraha Setya , Wibowo Yunus Aris , Saputro Hikari Dwi , Hapsari Karina Sindy
Published: March 2024

Natural disasters are phenomena that cause losses to humans. Therefore, research on disaster education must be intensified in areas where natural disasters frequently occur. Tawangmangu is an area that is prone to landslides. This research aims to analyze landslide vulnerability in Tawangmangu Sub-District, determine the distribution of schools in landslide-prone areas, and the school community’s knowledge of the Disaster Safe Education Unit (SPAB). This research is qualitative research based on literature studies, school locations, landslide disaster vulnerability data as well as policies and history of SPAB implementation in Tawangmangu. Determining the research location based on the level of landslide vulnerability in Tawangmangu which shows that most of the area is in the high vulnerability category. The research results show that the actual condition of schools prone to landslides in Tawangmangu is 54%, but individual knowledge about SPAB is still very low. The SPAB program needs to be implemented in schools prone to landslides in Tawangmangu Sub-District.
Disaster risk assessment in villages near PT ANTAM UBPB West Kalimantan’s bauxite mining area
Authors: Abdillah A M , Sulakhudin  , Sukirno  , Fermata W , Sari E P , Nasruddin
Published: March 2024

PT Antam UBPB West Kalimantan is a firm dedicated to the long-term viability of the local community. This commitment is fulfilled by the preparation of an analysis of disaster risk, vulnerability, and capacity owned by the communities surrounding the company’s operational region in order to lessen the danger of an imminent disaster. Balai Belungai Village is one of these villages, which faces two sorts of threats: floods and droughts. According to an estimate of disaster risk, a flood will have an impact on four sorts of assets: human assets, economic assets, infrastructure assets, and environmental assets. Drought will have an impact on four sorts of assets: human assets, economic assets, environmental assets, and social assets. Drought poses a high level of danger to economic assets, a moderate level of risk to human and environmental assets, and a low level of risk to social assets.
Gravity signatures at the upper plate above subducting asperities along Sunda Arc
Authors: Handayani Lina , Hanif Muhammad , Mukti M Maruf , Anggono Titi , Lubis Ashar M. , Gaol Karit L , Sudrajat Yayat , Setiawan Iwan , Arisbaya Ilham , Triahadini Agnis
Published: March 2024

The subducting Indo-Australian plate along the Sunda Arc has various degrees of roughness near the trench. The variation in the subducted plate’s seafloor morphology should affect the upper plate’s dynamic at the forearc region and further in land, which would appear in the form of variation in seismicity along the arc. In this paper, we explored the pattern in topography and gravity along the forearc at two regions. First is northern Sumatra, where some ridges have been subducted. The second is east Java, where part of the oceanic plateau of Roo Rise has been subducted. In addition, we also examined the seismicity in those two regions. The elongated ridges pattern of the subducted ridges was observed in the forearc region of Sumatra as the higher anomalies. It is probably due to the continuous nature of the ridge. The broader ruggedness, such as in Roo Rise, does not reflect directly at the Java forearc. However, the anomalies are chaotic and might represent the condition of the rugged subducted plate. The seismicity events at the area of the subducted ridge formed a straight line at the same position where the ridges are assumed to be. Meanwhile, the events on the opposite side of the plateau showed more sporadic distribution, which might indicate the condition of the irregular morphology of the subducted plate.
Geological, geophysical, and geotechnical approaches on the slip surface investigation on Cisumdawu Highway, West Java, Indonesia
Authors: Santosa Imam , Zakaria Zufialdi , Sumaryono  , Mulyadi Eddy , Praja Nia Kurnia , Arifianti Yukni , Patria Cahya
Published: March 2024

This study used geological, geophysical, and geotechnical methods to investigate the causes of landslides in a newly established Cileunyi Sumedang Dawuan (Cisumdawu) Highway, Sumedang Regency, West Java Province. A combination of geological field observation, subsurface geoelectric resistivity-based survey, and borehole drilling were applied to obtain the subsurface data of a research area. A total of 10 boreholes were drilled to collect geoengineering data, including the soil material and N-SPT value for validation purposes. The soil hardness and resistivity were measured and compared to establish the relationship between resistivity and engineering properties. The result revealed that percolating water zone in the permeable loose soil was located above the impermeable layer, estimated as a slip surface. The analysed subsurface measurement and borehole data showed that the depth of the slip surface is between 26-27m. The study is important to determine the mitigation steps for the highway slope stability construction.
Prototyping of Smart School Emergency Tents (SADAR) modular-based for post disaster recovery in education
Authors: Purwanto  , Farihah Siti Nur , Sofiana Eva Indra , Nugroho Laksono Dwi , Irawan Listyo Yudha , Aripriharta  , Fauzan Slamet
Published: March 2024

Disasters in Indonesia caused damage to school facilities and their conversion into refugee camps, so that teaching and learning activities were disrupted. Meanwhile, students still need learning activities such as trauma healing and post-disaster learning continuity. Emergency tents that are not yet effective and efficient do not fulfill the eligibility criteria for teaching and learning activities regarding safety, comfort and conduciveness. They have not utilized alternative energy to support learning activities. A hampered learning system can reduce learning capacity and delay the delivery of material to students. This research aims to create innovations related to post-disaster recovery in the education sector so that learning activities are not disrupted due to disasters. The research method applied is R&D (Research and Development) with a 4D model (Define, Design, Develop and Disseminate). The result of this research is a Smart Emergency School Tent (SADAR) Prototype Model with an effective and efficient installation design. This modular tent uses polycrystalline type solar panels as its power source and a knock-down multifunctional portable table that can be used as a table and bed. The emergency school tent design innovation is made flexible, structured, and can function as trauma healing for students.