bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maternal swimming with overload allied to postnatal high-fat, high-sugar diet induce subtle impairment on rat offspring’s ovarian redox homeostasis
Authors: Mariño Dal Magro Bárbara , dos Santos Bernardo Gindri , August Pauline Maciel , Menegotto Manuela Zeferino , Driemeier David , Matté Cristiane 
Published: December 2024

Context The Developmental Origins of Health and Disease (DOHaD) concept suggests that early-life interventions significantly influence the long-term health outcomes of offspring. Emerging evidence supports that maternal physical exercise and balanced nutrition can positively impact the health of the next generation. Aims This study investigated the effects of maternal swimming combined with postnatal high-fat, high-sugar (HFHS) diet on the ovarian health of adult female Wistar rat offspring. Methods Adult female Wistar rats performed swimming exercise in a controlled temperature environment (32°C, 2% bodyweight overload adjusted daily) during 4 weeks, starting 1 week prior mating. The female offspring received a control or HFHS diet from postnatal day (PND) 21 to PND 90. We analyzed offspring’s body weight, ovarian histomorphology, redox status, and associated molecular pathways 5′ adenosine monophosphate-activated protein kinase (AMPKα), forkhead box O3 (FoxO3), and mitofusin 1 (Mfn-1). Key results Our findings reveal that maternal swimming exerted an effect on offspring body weight gain, delaying it. Individually, maternal exercise reduced superoxide dismutase (SOD) activity and mitofusin-1 levels, while the postnatal HFHS diet alone decreased both SOD and glutathione peroxidase (GPx) activities and increased the pFOXO3a/FOXO3a ratio in the ovaries. Conclusions We showed that combination of maternal swimming with a 2% overload and a postnatal HFHS diet can negatively affect the ovarian redox balance in offspring. Implications Prenatal and postnatal lifestyle might affect reproductive function in females.
Anisotropic stellar solutions in general relativity: stability and physical viability via Buchdahl spacetime
Authors: Kumar Jitendra , Miglani Gazal , Chaudhary Sourav , Chandelkar Raj Singh 
Published: December 2024

Abstract
In this study, our aim is to generate the new anisotropic solution for compact stellar configurations with the help of Buchdahl geometry. We used the Buchdahl metric potential [J. Astrophys. Astron. 3, 325 (1982)] to deal with the field equations in general relativity framework. To get the constant parameters, the exterior Schwarzschild de Sitter solution is linked to the interior solution at the boundary. We discuss the behavior of density, radial and tangential pressure for the present model. We take the star candidates 4U 1820-30, PSR J1903+327, 4U 1608-52, 4U 1538-52 and HerX-1 to complete our analysis. The proposed compact stars equilibrium and stability states are analyzed using the Tolman-Oppenheimer-Volkoff equation, causality condition, adiabatic index and Harrison-Zeldovich-Novikov Criterion, respectively. It is noticed that our proposed model is suitable and provides viable results with Buchdahl geometry.
Transitory metamaterials based on symmetrical splitting of rotating squares
Authors: Lim Teik-Cheng 
Published: December 2024

Abstract
Materials exhibiting a negative Poisson’s ratio, known as auxetic materials, have garnered significant interest due to their unique mechanical properties and potential applications. This paper introduces a new class of auxetic metamaterials based on modified interconnected rotating rigid squares, where each square can split into two or four isosceles right triangles. The study explores three models categorized by their order of splitting, ranging from purely rotating squares (zeroth order) to systems with sub-units exhibiting relative motion (first and second orders). Detailed analyses of the in-plane Poisson’s ratio for these models were conducted, focusing on both infinitesimal and finite deformations. The results reveal that the proposed metamaterials demonstrate a transition in Poisson’s ratio behavior, characterized either by discontinuity or continuous but non-differentiable Poisson’s ratio at the transitory state between deformation mechanisms. This transition highlights the potential of these metamaterials to exhibit tunable mechanical responses, offering insights into designing materials with customized properties for advanced engineering applications.
Study of thermochromic material and its properties for visual indicators
Authors: Král R , Fortin L , Zemenová P , Akouibaa M , Poupon M , Nicaise A , Šafrata J P , Šafrata D D , Chevalier J M , Pokorný J , Houdková J , Jiříček P , Lachkar M , El Bali B 
Published: December 2024

Abstract
This work deals with the preparation and characterization of inorganic hybrid phosphite salt (enH2)[Ni(H2O)6](HPO3)2, where “en” stands for ethylenediamine (C2N2H8). This material belongs to a broad family of chromogenic compounds, which may have wide applications as pigments for visual indicators due to their color change in response to external stimuli (e.g. temperature, pressure, radiation, etc.). Such pigment can be used to develop new smart textiles, smart windows, safety sensors for innovative packaging, etc. However, before that it is necessary to understand the origin of color transition and the impact of the transition on its environment. Therefore, the (enH2)[Ni(H2O)6](HPO3)2 was studied by various thermoanalytical (OTM, TMA, DTA, TG, EGA), structural (HT XRPD, XPS), and optical (Raman spectroscopy) methods. Several color transitions were identified in the range from 67 to 215 °C, accompanied either by decomposition, recrystallization, or both. The first color change (from a translucent blue to an opaque light green) occurred at 67 °C due to a release of crystalline H2O. The final color change (from a green to a dark blue) was at ca. 215 °C due to the formation of the new phases (NixP and Ni2P). HT XRPD, Raman spectroscopy, and XPS observed the evolution of the crystallinity state and chemical bonding. Finally, the kinetic parameters (activation energy and reaction rate) of the first decomposition effect were determined via the Kissinger method.
Influence of specimen dimensions on the tensile behaviour of NiTi alloy
Authors: Losertová M , Kopelent M , Kratochvíl A , Kubášová K , Sedláček R , Kawuloková M , Zlá S , Adamczyk-Cieślak B 
Published: December 2024

Abstract
This work investigates the effect of specimen dimension on the tensile behaviour and superelasticity plateau of NiTi alloy with the composition of 56.31 wt.% Ni and 43.69 wt.% Ti. The development of stress-strain curves in tensile testing was evaluated for two different dimensions of NiTi specimens. The resulting properties of the testing material were discussed in relation to the phase composition determined by EDX, XRD, and DSC analysis.
Cherenkov real-time monitor for proton flash therapy
Authors: Akulinichev Sergey , Gavrilov Yuri , Djilkibaev Rashid 
Published: December 2024

Abstract
Flash therapy with ultra-high dose rate beams opens up the
possibility of significantly reducing radiation damage to normal
tissues. But that type of radiotherapy requires the development of
new methods for dosimetry and monitoring of beams, since many
conventional detectors are not fully suitable for these purposes. In
this work, the characteristics of a new proton beam monitor that
detects Cherenkov radiation from secondary delta electrons were
studied in flash mode of irradiation. This monitor is applicable for
proton beams of any intensity, very transparent for fast protons,
and can provide real-time control of the absorbed dose and beam
position. To record Cherenkov radiation, generated in a thin glass
radiator located in the beam in front of the target, we used
single-channel and multichannel photomultipliers. Proton beams with
an energy of 160 MeV and a current of 1 mA were studied. The
readings of a Cherenkov monitor, EBT-XD films, and a beam current
transformer were compared and consistency of these detectors was
found. The linearity of the Cherenkov monitor readings vs dose is
very high (R
2 = 0.9995). This monitor can be used for real-time
control of the absorbed dose and of the beam position in flash
therapy for any intensity of proton beams.
Declining resilience of forest carbon sinks linked to increased atmospheric water deficit during droughts in the Northern Hemisphere
Authors: Wu Xiaoyun , Xu Hang , Pang Jianzhuang , Zhang Zhiqiang 
Published: December 2024

Abstract
Droughts have posed intense threats to the forest carbon sink (i.e. net ecosystem productivity, NEP), potentially elevating the risk of forest degradation and challenging the achievement of climatic and ecological goals. While global forest NEP endured, the resilience of NEP served as the ability of ecosystems to withstand and recover from perturbations and the underlying maintenance mechanisms during droughts remain unclear. Here, we explored the responses of NEP resilience, quantified by the lag-1 temporal autocorrelation coefficient (TAC) of two consecutive time series, to droughts based on 87 drought events across 45 forest sites with flux and meteorological observations in the Northern Hemisphere. Furthermore, an interpretable machine learning algorithm was utilized to disentangle the synergistic effects of environmental and biotic factors on TAC, achieving a mean coefficient of determination of 0.87 for drought events with significantly increasing TAC and 0.91 for other drought events. Here, we found that the increase in NEP resilience could alleviate the negative effects of droughts, in contrast to a 2.5 times increase in the probability of NEP decline events associated with decreased NEP resilience. However, NEP resilience declined with the rise of drought intensification. The reduced reference canopy conductance (G
cref) was the primary constraint on NEP resilience maintenance, contributing 48% to the total influence of biotic factors. In addition, high vapor pressure deficit (VPD) exacerbated the negative effects of soil moisture deficit, jointly leading to the decline in NEP resilience. Specifically, elevated VPD during droughts significantly reduced G
cref, indicating the vulnerability of tree hydraulic systems to compound stress. Overall, our study emphasizes the potential risks of the compound soil and atmospheric water deficit on forest NEP resilience and carbon sink across the Northern Hemisphere in the future.
Young Researchers School 2024 Maynooth: Lectures on CFT, BCFT and DCFT
Authors: Northe Christian 
Published: December 2024

Abstract
These notes were presented at the Young Researchers School (YRS) in Maynooth in April 2024 and provide an introduction to Conformal Field Theory CFT, Boundary Conformal Field Theory (BCFT) and Defect Conformal Field Theory (DCFT). This class is mostly self-contained and includes exercises with solutions. The first part of these notes is concerned with the basics of CFT, and was taught by the author during the pre-school for the YRS 2024. Here the aim is to convey the notion of conformal families, their fusion and the construction of partition functions. The second part of these notes is dedicated to boundaries and defects in CFT and was presented by the author at the main school. As far as boundaries are concerned, emphasis is placed on boundary operators and their state spaces, as well as the boundary state formalism with the Cardy constraint. Topological defects are discussed in analogy, i.e. defect state spaces and the relevant consistency constraint are derived. Verlinde lines are constructed as their simplest solution and their properties are inspected.
Exploring forest disturbance detection using Sentinel-2 imagery: A case study at FRIM campus
Authors: Muhamad Afizzul M. , Hamdan O. , Tan S.A. 
Published: December 2024

Abstract
Monitoring forest disturbances is crucial for the preservation of biodiversity, climate change mitigation, and overall ecosystem health. Remote sensing offers a powerful tool for monitoring forest disturbances as it can cover large areas, detect changes in near real-time, and cost effective as compared to survey method. Thus, this study tries to utilize the use of Sentinel-2 imagery to detect forest disturbances in Forest Research Institute Malaysia (FRIM) campus. Continuous change detection and classification (CCDC) algorithm was employed to identify disturbances that occurred between 2019 and 2023. NDVI, NDMI, and NBR were also used to improve the accuracy of disturbance detection. The study returned a promising result, achieving a precision of 85 percent in detecting forest disturbances. The effective utilization of Sentinel-2 imagery and the CCDC method in this study highlights not only the efficiency of remote sensing in precisely detecting forest disturbances but also underscores its potential to contribute significantly to sustainable conservation initiatives.
Automatic extraction of Kuala Terengganu coastline using google earth engine (GEE) by automated water extraction index (AWEI)
Authors: Khurram Saima , Pour Amin Beiranvand , Bagheri Milad , Ariffin Effi Helmy , Akhir Mohd Fadzil 
Published: December 2024

Abstract
The study aimed to evaluate changes in Kuala Terengganu’s coastline area from 2014 to 2023 using Google Earth Engine (GEE) and satellite-based images. The approach used automated satellite image searches and JavaScript to analyze specific regions, avoiding the need for downloading whole images. The GEE API code, which incorporates the Automated Water Extraction Index (AWEI) and Otsu Thresholding algorithms, was used to capture small shifts in the shoreline over time. The study demonstrated the global applicability of GEE for automating coastline extraction and object identification using satellite imagery. The research demonstrated the effectiveness of using satellite imagery and GEE to monitor the entire shoreline, providing crucial data for coastal adaptation and management. The study underscores the role of GEE and its API services in advancing automated shoreline extraction technologies, providing valuable insights for environmental research and coastal management.
Nickel ore export prohibition and mapping the business performance of nickel mining companies in Indonesia
Authors: Hati S N A P , Hapsari M A , Ika S R , Widagdo A K 
Published: December 2024

Abstract
As part of its efforts towards establishing the green energy value chain internationally, Indonesia has banned the export of raw nickel since 2020. The export ban is also aimed at adding value to the nickel supply chain for the sake of nickel mining sustainability in the country. The nickel ore export prohibition has caused the European Union to file a lawsuit against the Indonesian government in violation of the World Trade Organization agreement. Instead of discussing the dispute, this study will focus on whether the export ban will impact the business performance of the nickel mining industry. Given that the country is the biggest nickel reserve on earth, this study aims to accomplish three distinct objectives: to create a thematic map of (1) nickel mining locations in Indonesia by province; (2) mining locations for the top ten nickel companies and nickel industry hubs in Indonesia; and (3) the business performance of leading nickel issuers in the country. Thematic maps are created based on Geographic Information Systems (GIS). The results show that nickel reserves are available in seven provinces; two nickel industrial hubs and ten big mining companies operate in these provinces; and these companies have sound business performance following the ore nickel export ban. Our analysis offers a new perspective on the effect of the national nickel policy on the business viability of selected mining companies in Indonesia by applying the company’s financial healthiness measurement in GIS.
Estimation of Sound Speed Profile at Southbound of South China Sea For Bathymetric Survey Using Machine Learning
Authors: Abdullah Muhamad Khairul bin , Yuhaoa Ang , Mohd Shafri Helmi Zulhaidi bin , Mohd Akhir Mohd Fadzil bin , Mohd Azmi Nuur Hakeem bin , Al Habshi Mohammed Mustafa Abdulrahman , Mat Dani Mohd Ikmal Fama bin 
Published: December 2024

Abstract
Bathymetric survey is the method done to acquire the depth of sea and understand the shape and layout of the underwater seabed by hydrographic surveyor for safety of navigation. To conduct bathymetric survey one of the main components are sound speed profile. The current practice faces many difficulties like the sensor covered by biofouling over time, sensor defects occurrence and laborious efforts. Thus, conducting post processing are the suggestion with creating a new model using Machine Learning (ML). The model is created using historical In-Situ data collected by National Hydrographic Centre as responsible agency updating nautical products for safety of navigation in Malaysian water. In this study, the area of study was done in Southbound of South China Sea and propose new method of estimating Sound Speed Profile using ML. The study describes the preprocessing steps, followed with spatial distribution of Sound Speed Profile Values using Inverse Distance Weightage (IDW). Then, based on the historical data, the data are divided between training and test dataset. Training dataset were experimented with 4 ML algorithm which is Decision Tree, Random Forest, Linear Regression and Gradient Boosting. With Random Forest showing the best result with R2 value of 0.998. After fine tune the Training Dataset, the prediction is done using test dataset which bring a final result of 0.905. Although the finding not intended to replace the real time data, the model provide hydrographic surveyors with valuable tool for conducting post-processing with more accurate estimation of sound speed profile using historical in situ data.
Optimization of in-car-abandoned children detection model via deep learning approaches
Authors: Norman Masayu , Ahmad Shukri Ainaa Ilyana , Mohammad Hafiz Ismail , Mohamad Zuraihan , Rahim Ashnita , Fazly Amri Mohd , Mohd Shafri Helmi Zulhaidi 
Published: December 2024

Abstract
Children being trapped in vehicles is a critical issue that has led to many deaths. The number has increased dramatically regardless of the precautionary measures taken by their parents and guardians, which not only become a local but also a global concern. Though several technologies and systems have been developed to identify the in-car-abandoned children (iCAC), there are still reported cases occurring every day due to several factors. Deficiencies in the existing device need to be identified and improved to detect the children precisely. Over the past few years, the use of deep learning (DL) algorithms for remote sensing image analysis has risen, and several intelligent and efficient detection algorithms have been proposed. Yet, the improvement strategies DL-based object detection has never been summarized in detail. This paper will evaluate the accuracy of iCAC’s images taken from the ground platform using DL algorithms. Besides, the hyperparameter is tuned to optimize detection accuracy. Convolutional Neural Networks (CNNs) and MobileNet V2 are two types of DL image classification models utilized in this study. Both models were used to extract features and train the three categories of child, no-child, and adult. Next, the classification outcomes are compared, and the model’s effectiveness is evaluated. Learning rate, epoch and batch size were applied and tested to examine the change in the model’s accuracy rate for the model’s optimization. The results obtained demonstrated that the accuracy of children’s image detection could be improved by optimizing the DL algorithms. This research contributes valuable insights into real-time image detection because the DL models have shown promising results in advancing the capabilities of image detection. The findings are significant to the ongoing optimization of remote sensing techniques for accurate and efficient image recognition.
Multi-Sensor Satellite Data Analysis for Urban Land Use Pattern Recognition
Authors: Gautam Anita , Aithal Bharath H. 
Published: December 2024

Abstract
A global phenomenon, urbanization is characterized by the expansion of cities and rapid growth of urban areas. As cities continue to grow, assessing the distribution and trends in land use is increasingly essential to identify, connect, quantify, validate, and predict the negative consequences of urban areas on the environment. In recent decades, the availability of multi-sensor data has revolutionized and emerged as a promising approach for improving efficiency and precision, and each sensor provides distinct capabilities as well as characteristics that make it possible for an in-depth assessment of urban land use. In this context, this research aims to understand the optical data for urban land use mapping using high-resolution remote sensing satellite imagery of different periods. Traditional and machine learning techniques are used to classify urban land use patterns. The results revealed a major land use change with more built-up regions. Urban land area increased from 36.52 % (2012) to 60.23 % (2023), indicating significant urbanization. Additionally, random forest outperformed methods in comparison analysis. Moreover, the significance of this present study is that it offers insightful information into structural and spectral features of urban areas and emphasizes how precise land use classification is crucial for efficient land management, urban planning, and sustainable urban development. The study also exhibits different sources of data from different types of sensors, indicating the possible usage under any condition for emergency monitoring and immediate response.
Climate Change Impact Assessment Based on Perception and Participation in Telagah Village
Authors: Rahmawaty  , Ismail Mohd Hasmadi , Rauf Abdul , Gandaseca Seca , Karuniasa Mahawan , Kim Yekyoum 
Published: December 2024

Abstract
Rural communities like Telagah Village often have economies and ways of life deeply intertwined with their natural environment. They were particularly vulnerable to the impacts of climate change due to their dependence on agriculture and forestry, which are directly affected by changes in weather patterns, temperature, and precipitation. This study aimed to assess community perceptions and participation regarding the impacts of climate change in Telagah Village, Sei Bingei Sub-district, Langkat Regency, North Sumatra Province, Indonesia. In-depth interviews were conducted with eight key informants who had lived in this village for a long time. Data analysis was carried out descriptively. The results of interviews with key informants in Telagah Village show that the most influential impacts of climate change are drought, uncertain weather conditions, and high rainfall. Forest land clearing is the main cause of climate change in this area. Community participation and perception in efforts to prevent the impacts of climate Community participation and perception in efforts to address climate change impacts are generally positive. Perceptions are very good (37.5%) and good (62.5%). Reforestation and forest conservation efforts have been carried out. Through its focus on Telagah Village, this research contributes valuable insights into the localized effects of global climate change, offering a basis for informed decision-making and sustainable development strategies in similar rural settings. Understanding the specific impacts of climate change on Telagah Village enables the development of tailored adaptation and mitigation strategies. It allows for targeted interventions that address the unique vulnerabilities and capabilities of the community, enhancing resilience to climate-related stresses.