bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification of Tidal Patterns in Malaysian Maritime Regions: Integrating from Multi-Mission Satellite Altimeter, Tidal Data and Hydrodynamic Model
Authors: Alihan N S A , Din A H M , Hamden M H , Wijaya D D 
Published: December 2024

Abstract
The exploration of tidal variability holds fundamental significance in various practical applications such as safety navigation, port operations, coastal management, environmental planning, and scientific studies. Prior to the implementation of satellite altimeters, tide gauges functioned as the primary data sources for tidal measurements. However, the availability of tidal information was constrained, particularly in the offshore areas, owing to the sparse distribution of the tide gauge stations predominantly installed along coastal regions. Although the utilisation of advanced satellite altimetry ensures high resolution in spatio-temporal assessment, the accuracy of the derived tidal model degrades significantly near coastal areas due to complex tidal interactions influenced by hydrodynamical effects. Thus, this research undertakes a comprehensive study of tidal characteristics in Malaysian waters through the integration of diverse data sources. In this study, the multi-mission satellite altimeter, tidal data, and a hydrodynamic model are employed synergistically to estimate the major tidal constituents (K1, M2, S2, and O1) using harmonic analysis and tide model driver. Then, the tidal form factor is determined to elucidate the tidal patterns prevalent in the study area. The results contribute to the understanding of tidal behavior and provide valuable perspectives that are instrumental for infrastructure planning, environmental sustainability and maritime operations in the Malaysian maritime regions.
Flood susceptibility assessment using multi-criteria analysis: Case Study in Selangor, Putrajaya, and Kuala Lumpur
Authors: Ibrahim Illyani , Ghani Ngahzaifa Ab , Asmawi M Zainora 
Published: December 2024

Abstract
This study discusses flood assessment that makes use of the geographic information system (GIS) technology to provide a thorough identification of flood susceptibility zones in Selangor, Kuala Lumpur, and Putrajaya. Using MCA-GIS, flood susceptibility zones were delineated based on environmental factors such as precipitation, TWI, distance from the river, elevation, slope, land cover, and flow accumulation. To verify the framework’s accuracy and dependability, comparisons with historical flood events were used to validate the flood susceptibility zones. The findings indicate that almost 13.9% of the study area is extremely susceptible to flooding, with the highest risk concentrated in the upstream and downstream regions of the Selangor River. Only 0.1% of the region was deemed to be at very low risk, indicating widespread flood susceptibility. As over 46% (very high susceptibility and high susceptibility) of the study areas are classified as flood-prone areas, stakeholders must prioritise appropriate precautions to lessen the impact of catastrophic flood events. The generated flood susceptibility zone maps can be used by urban planners and risk management professionals as a crucial source of information.
Development of automated UAV LiDAR based blueprint in oil palm replanting on terraces
Authors: Naito Chihiro , Takeuchi Wataru , Cheak Sim Choon , Subramaniam Vijaya 
Published: December 2024

Abstract
Malaysia, which is the second largest palm oil producing country in the world, has faced the challenge of labor shortage to sustain palm oil production. Measuring replanting locations for palm seedlings manually in the field is a labor-intensive task, especially in sloping areas with terraces. However, algorithms or freely available software is limited. Therefore, this study aimed to develop programs to automate the generation of replanting points for palm seedlings, particularly on terraces in sloping areas using Python programming. Terraces were detected using a deep learning approach from a Curvature and Slope (CS) image generated from UAV LiDAR-based DEM. A series of processing steps were applied to remove error terrace lines and to refine them. On refined terraces, planting points were successfully generated automatically. The generated planting points were compared with the previous palm points. The results indicated that areas around the hilltop or foot of the hill, where the CS image was less distinct, tended to fail in generating points. Additionally, short terrace lines were unintentionally eliminated during processing. Although the programs need improvement, the proposed method successfully generated points across the entire study site, which is expected to reduce labor costs greatly. Further improvement of the program, additional training for the deep learning model, and trials of the proposed method at different sites are needed to enhance its robustness and applicability.
Biodiversity data crowdsourcing: a way forward.
Authors: Azmy Suzanna N. , Tarmidi Zakri , Hassan Noordyana , Johari Shazrul Azwan , Shamsir Omar Mohd Shahir 
Published: December 2024

Abstract
This study addresses the underutilization of crowdsourcing, particularly involving citizen scientists, for biodiversity data collection in Malaysia. While high-income countries have embraced crowdsourcing to enhance scientific knowledge and inform conservation efforts, Malaysia lags behind due to the absence of a regulatory framework guiding government and citizen scientists. The absence of such a framework limits data collection to government agencies and university researchers, impeding the progress in biodiversity management. This paper focused on the investigation of the current system for biodiversity inventory and the requirements of a mobile crowdsourcing system for forest stakeholders in Malaysia, specifically in Johor. In addition to requirements, the perception of forest stakeholders’ representatives towards data sharing, crowdsourcing and citizen science also has been scrutinized.
Artificial Intelligence for rapid mapping of potential archaeological features using Bag of Visual Words based image classifier
Authors: Roslan Shairatul Akma , Abd Rahim Muhamad Sharifuddin , Yakub Fitri , Kong Yong Chee , Noor Norzailawati Mohd 
Published: December 2024

Abstract
Integrating Artificial Intelligence technological advancements in archaeology has revolutionised automated feature detection, presenting a novel perspective on archaeological feature recognition and image interpretation. This approach reduces costs associated with ground data collection and enhances the reliability and productivity of large-scale archaeological mapping. Consequently, this study aims to explore feature detection and matching techniques in archaeological detection using Artificial Intelligence and Scale-Invariant Feature Transform and Oriented Fast and Rotated Brief algorithms, which are frequently employed in image processing applications as a feature descriptor within the Bag-of-Visual-Words framework. The high-resolution multispectral satellite SPOT image maps potentially hidden archaeological features in Bujang Valley, Kedah, Malaysia. The expected outcome involves presenting a BoVW model capable of accurately detecting hidden archaeological features within the generated maps, thereby providing valuable insights into the extent and distribution of archaeological remnants in the targeted regions.
Mapping urban road networks using semantic approach
Authors: Dey  , Aithal Bharath H. 
Published: December 2024

Abstract
Over the last decades, urbanization and its impact on the demand for infrastructure have drawn much attention in global studies and policymaking. Road infrastructure has contributed to a compound annual growth of approximately seven percent. This quick growth of road infrastructure has brought several challenges. Meanwhile, with the availability of high-resolution remote sensing images, extraction of accurate road information has become a fundamental challenge in image processing and geospatial-related technologies. This study proposes a novel framework for road extraction and automated vectorization process. The proposed model excavates contextual information from high-resolution images to restore the topological information of road features. Further, we evaluate our model’s extraction capability on online and acquired datasets. The experimental results demonstrate that the proposed model outperforms the state-of-the-art architectures by obtaining 97.42% and 97.18% overall accuracy and 77.15% and 72.66% IoU for the Ottawa Road Imagery and Indian dataset, respectively. The extracted road features are further mapped, restoring the geospatial information. This analysis would help develop the database for the urban observatories and help sustainably plan future developments.
Time-series analysis of carbon density over Malaysia’s forests using open-access Landsat imagery on Google Earth Engine platform
Authors: Omar Hamdan , Misman Muhamad Afizzul 
Published: December 2024

Abstract
The production of high-resolution carbon density map at national level requires substantial amount of data and time-consuming. This study presents estimates of aboveground carbon density (ACD) changes over the forests that have occurred in the previous three decades in the State of Johor, as a case study. The entire process was streamlined by Google Earth Engine (GEE), which makes it effective and scalable. The ACD estimation was produced based on the Aboveground Carbon Density Indicator (ACDI) generated from Landsat imagery datasets. Based on the estimates, a 30-metre resolution, wall-to-wall map of AGC across the entire forested region of Johor has been created. Forest cover carbon density changes have been assessed for the period between 1990 and 2023, with a chrono sequence of years 1990, 2010, 2016 and 2023. Results indicated that the ACD has reduced 28.49 million Mg C, which was from 113.4 to 84.91 million Mg C in years 1990 and 2023, respectively. The high-resolution maps products offer valuable information for various stakeholders, with important implications for carbon sequestration efforts, conservation goals, and sustainable forest management. The methodology demonstrated the effectiveness of using open-access datasets and open-source GEE technologies to generate AGC information at the sub-national and national levels. This research will eventually increase our understanding of carbon dynamics in Malaysian forests and stimulate the implementation of realistic forest management and conservation practices in order to use forest resources responsibly and mitigate the consequences of climate change.
Prediction of monthly rainfall in selected African stations using deep learning algorithms
Authors: Samson T. K. , Aweda F. O. , Omoliki A. J. , George G. C. , Oladapo O. F. , Ogunwale E. I. 
Published: December 2024

Abstract
Precipitation has a huge impact on human life and culture. Agriculture and public infrastructure is dependent on accurate rainfall forecasting, which can aid in water supply planning, reservoir management, and flood mitigation. In view of the climate change, it is very imperative to provide a reliable forecast of rainfall leveraging on Deep Learning algorithms. This study therefore compared the forecasting performance of five different Deep Learning (DL) algorithms in predicting rainfall in five African stations (Abuja, Cairo, Nairobi, Pretoria, and Yaoundé). Deep Learning algorithms considered in this study include: Long Short Term Memory (LSTM), Convolutional Neural Network CNN, K-Nearest Neighbor’s Regression (KNNR), and Multilayer Perceptron (MLP). These algorithms were trained using data on monthly rainfall in these stations between 31st January, 1980 and 31st December, 2022 as obtained from Helioclim-1 archives. Data were preprocessed and 80% of the data were used in training and 20% in validation. The forecasting performances of these algorithms were evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Result showed that although these DL algorithms were competitive in terms of their performance, MLP was found to be most suitable of these algorithms for Cairo (RMSE =5.0246), Pretoria (RMSE =47.4608) and Yaoundé (RMSE = 140.1368) while for Abuja (RMSE = 88.7868) and Nairobi (RMSE =95.3637), result favoured KNN and CNN respectively. As a result, the study concluded that MLP is superior for the majority of stations. KNN excelled in Abuja, CNN in Nairobi, and MLP in Cairo, Pretoria, and Yaoundé, indicating that each station benefits from a unique deep learning algorithm.
The determinants of handwashing in humanitarian crisis setting during the COVID-19 pandemic: a multi-country analysis
Authors: Lamb Jenny , Thorseth Astrid Hasund , MacDougall Amy , Thorsen William , White Sian 
Published: December 2024

Abstract
The Wash’Em process was developed to improve the design of handwashing behaviour change programmes during outbreaks and humanitarian crises. It aims to rapidly create evidence-based, contextualized handwashing programmes. Wash’Em was widely used during the COVID-19 pandemic. This multi-country secondary data analysis compares data emerging from Wash’Em during the pandemic, to understand whether commonalities in programming constraints or the determinants of handwashing behaviour existed across countries. Wash’Em datasets (n = 38) were verified prior to inclusion in secondary data analysis; descriptively summarized and then statistical summaries of homogeneity were derived. Wash’Em was implemented as intended during the pandemic, typically taking a small number of humanitarian staff less than a week to complete. Most actors reported using the recommendations suggested by the process but did so within relatively short-term and poorly financed prevention programmes. Homogeneity in the responses to the Wash’Em tools was low indicating that the determinants of handwashing behaviour during the pandemic were predominantly shaped by pre-existing factors rather than the nature of the health threat. Hygiene programmes during outbreaks should avoid ‘copying and pasting’ interventions from one setting to another and instead make time to holistically understand the behavioural determinants in a specific context and develop programme activities that are designed to address these. Particular attention should be given to factors in the physical and social environment that may enable or constrain handwashing behaviour, pre-existing disease vulnerabilities and the secondary and non-health impacts of outbreaks. Wash’Em provides one feasible way of contextualizing handwashing interventions in outbreak or humanitarian settings.
Beyond Connectivity: Exploring the Influence of Digital Access on the Social Determinants of Health for Youth in Out-of-Home Care
Authors: Gomez Rebecca , Tomlinson Camie , Reddish Naomi , Reiss Dana 
Published: December 2024

Abstract
This study explores digital inclusion for youth in out-of-home placement within the child welfare system, a group impacted by significant social determinants affecting health. Recognized as a “super social determinant of health,” digital inclusion holds promise in addressing key health factors, such as social relationships, support systems, and information access. The study surveyed 131 foster parents and 152 kinship providers across varied U.S. regions about access and use of technology among the youth in their care. Findings reveal that most of the youth in our study possess personal technological devices, and their caregivers facilitate access. Although the majority have access to technology, the rate is significantly lower than the general population of youth. Despite constraints contributing to a digital divide, caregivers note positive impacts, underscoring technology’s role in sustaining social connections and fostering relationships. Closing the digital gap assumes critical importance in advancing health equity for this vulnerable demographic, underscoring the imperative for comprehensive digital inclusion strategies within the child welfare system.
Exploring the genetic expression of Wickerhamomyces anomalus during biosurfactant production from waste cooking oil
Authors: Fernandes Natalia , Simões Luara , Dias Disney Ribeiro 
Published: December 2024

Abstract

Aims
Biosurfactants are valuable eco-friendly compounds with broad industrial applications, particularly when produced sustainably using yeast and renewable carbon sources. Despite the potential of yeast in biosurfactant synthesis, little is known about the specific gene expression changes underlying this process. This study investigates the genetic response of Wickerhamomyces anomalus CCMA 0358 to biosurfactant production using waste cooking oil (WCO) as a low-cost carbon source.

Methods and results
During a 0–12 h fermentation period, RNA (ribonucleic acid) sequencing revealed 829 differentially expressed genes in W. anomalus grown with WCO, suggesting targeted metabolic adaptations. Pathway analysis showed WCO’s significant impact on glycolysis, gluconeogenesis, and lipid biosynthesis. Gene ontology annotations further indicated adaptive responses in ribosome biogenesis and lipid metabolism, which are crucial for the efficient utilization of WCO. Notably, WCO induced the upregulation of very-long-chain fatty acid precursors and adjustments in glycolytic enzyme expression, both essential for biosurfactant production.

Conclusions
This study reveals, for the first time, the specific genetic pathways and metabolic adjustments that W. anomalus employs to produce biosurfactants from WCO. The increased expression of lipid metabolism enzymes and cell membrane components highlights a tailored adaptive mechanism for lipid-rich waste substrates, positioning W. anomalus as a promising candidate for sustainable biosurfactant production.
Turgor pressure affects transverse stiffness and resonant frequencies of buzz-pollinated poricidal anthers
Authors: Alvord Mitchell , McNally Jenna , Casey Cailin , Jankauski Mark 
Published: December 2024

Abstract
Several agriculturally valuable plants store their pollen in tube-like poricidal anthers, which release pollen through buzz pollination. In this process, bees rapidly vibrate the anther using their indirect flight muscles. The stiffness and resonant frequency of the anther are crucial for effective pollen release, yet the impact of turgor pressure on these properties is not well understood. Here, we performed three-point flexure tests and experimental modal analysis to determine anther transverse stiffness and resonant frequency, respectively. Dynamic nanoindentation was used to identify the anther storage modulus as a function of excitation frequency. We subsequently developed mathematical models to estimate how turgor pressure changes after the anther is removed from a flower, thereby emulating zero water availability. We found that anther stiffness decreased by 60% at 30 min post-ablation and anther resonant frequency decreased by 20% at 60 min post-ablation. Models indicated that turgor pressure in the fresh anther was ~0.2–0.3 MPa. Our findings suggest that natural fluctuations in turgor pressure due to environmental factors such as temperature and light intensity may require bees to adjust their foraging behaviors. Interestingly, the anther storage modulus increased with excitation frequency, underscoring the need for more sophisticated mechanical models that consider viscous fluid transport through plant tissue.
Multi-omic profiles of Sorghum genotypes with contrasting heat tolerance connect pathways related to thermotolerance
Authors: Watson-Lazowski Alexander , Cano Francisco Javier , Kim Mikael , Benning Urs , Koller Fiona , George-Jaeggli Barbara , Cruickshank Alan , Mace Emma , Jordan David , Pernice Mathieu , Warren Charles , Ghannoum Oula 
Published: December 2024

Abstract
Understanding how crop varieties acclimate to elevated temperatures is key to priming them for future climates. Here, we imposed a 6 d heat shock treatment (reaching 45 °C) on two genotypes of Sorghum bicolor [one sensitive to heat shock (Sen) and one tolerant (Tol)] growing under two temperature regimes, and carried out a suite of measurements before and during the heat shock. Sen consistently reduced photosynthetic functioning during heat shock, while Tol increased its photosynthetic rate. Higher abundance of heat shock protein transcripts and metabolites related to heat tolerance were noted for Tol when compared with Sen both before and during heat shock, which can be attributed to constitutive and inducible responses to elevated temperatures. In addition, important changes in metabolic pathways were clearly identified for Tol during heat shock (including up-regulation of raffinose family oligosaccharides and down-regulation of the γ-aminobutyric acid catalytic pathway), even as the concentration of hexose sugars became depleted. We infer Tol was able to tolerate elevated temperatures due to up-regulation of osmoprotectants, chaperones, and reactive oxygen species scavengers and by the suppression of SnRK1 via transcripts and metabolites during heat shock. Our results highlight potential targets for attributes of high temperature tolerance that can be utilized in future breeding trials.
Cosmic quenching and scaling laws for the evolution of supermassive black holes and host galaxies
Authors: Xu Zhijie 
Published: December 2024

ABSTRACT
Observations suggest a co-evolution of supermassive black holes (SMBHs) and host galaxies. In this paper, we consider the mass and energy flow in a near-equilibrium bulge suffused by gases of varying temperatures. By assuming the rate of energy flow independent of the distance r from the bulge center and the local virial equilibrium for permeated gases on scale r, a key parameter $\varepsilon _b$ was identified that quantifies the mass and energy flow in gases and the efficiency of gas cooling (or the "specific" cooling rate per unit mass), and thus regulates the co-evolution of both SMBHs and hosts. With the help of Illustris simulations and observations, we determined the redshift variation $\varepsilon _b\propto (1+z)^{5/2}$. A higher $\varepsilon _b$ in the early Universe means a higher specific cooling rate that allows rapid evolution of SMBHs and hosts. This simple theory, characterized by a single parameter $\varepsilon _b$, provides the dominant mean cosmic evolution of SMBHs and hosts. All other transient phenomena may only contribute to the dispersion around this mean evolution. Based on this theory and relevant assumptions, scaling laws involving $\varepsilon _b$ were identified for the evolution of SMBHs and hosts. For host galaxies, the mass–size relation $M_b\propto \varepsilon _b^{2/3}r_b^{5/3}G^{-1}$, the dispersion–size relation $\sigma _b^2\propto (\varepsilon _b r_b)^{2/3}\propto (1+z)$, or the mass–dispersion relation $M_b\propto \varepsilon _b^{-1}G^{-1}\sigma _b^5$ were identified, where $r_b\propto (1+z)^{-1}$ is the bulge size. For SMBHs, three evolution phases were found involving an initial rapid growth stage with a rising luminosity $L_B\propto (\varepsilon _b M_{\rm BH})^{4/5}$, a transition stage with a declining $L_B\propto \varepsilon _b^2 M_{\rm BH} \propto (1+z)^5$, and a dormant stage with $L_B\propto (\varepsilon _b M_{\rm BH})^{4/3}$. Our results suggest a rapid initial super-Eddington growth in a short period with a new redshift-dependent luminosity limit $L_X\propto\varepsilon _b^{4/5}M_{\rm BH}^{4/5}G^{-1/5}c$, in contrast to the Eddington limit. Analytical solutions were formulated for the BH mass function $\Phi _{\rm BH}$, active galactic nucleus (AGN) mass function $\Phi _{\rm AGN}$, and duty cycle U that predict $\Phi _L\propto L^{-1/5}$ for the faint-end luminosity function, $\Phi _{\rm AGN}\propto M^{-1/5}$ for small-mass-end AGN mass function $\Phi _L$, and $U\propto M^{-1/5}$ at high redshift.