bluuebunny/crossref_metadata_2025 huggingface dataset:
Fluid cross-flow characteristics of a partially filled cavity in deep marine carbonate reservoirs
Authors: Wang Kailun , Ren Jianfei , Cheng Wan , Lei Gang , Yu Yibing
Published: December 2024

The analysis of the fluid cross-flow characteristics in partially filled cavities is essential for characterizing the fluid flow behaviors in deep marine carbonate reservoirs. Thus, it is vital for the effective development of crude oil in deep marine carbonate reservoirs. This study presents mathematical models for portraying the fluid cross-flow characteristics in partially filled cavities, considering the effects of gravity, cavity filling degree, and storability ratio. By solving the models using the Laplace transformation and Stehfest numerical inversion methods, pressure transient analysis, and rate transient analysis can be performed on the reservoir, resulting in the evaluation of the dimensionless pressure, dimensionless cross-flow rate, and dimensionless cumulative cross-flow rate in a cavity. The available experimental data adequately validate the proposed models. Moreover, based on the derived model, parameter sensitivity analysis is conducted. Based on the results, the cross-flow characteristics are significantly affected by fluid and cavity parameters. Additionally, as the thickness-to-radius ratio of the cavity decreases, the radial flow becomes pronounced, leading to a more complicated cross-flow process. The derived model not only assists in understanding and predicting the cross-flow characteristics in partially filled cavities in deep marine carbonate reservoirs but also provides guidance for crude oil exploitation.
Majorana quasiparticles and topological phases in 3D active nematics
Authors: Head Louise C. , Negro Giuseppe , Carenza Livio N. , Johnson Nathan , Keogh Ryan R. , Gonnella Giuseppe , Morozov Alexander , Orlandini Enzo , Shendruk Tyler N. , Tiribocchi Adriano , Marenduzzo Davide
Published: December 2024

Quasiparticles are low-energy excitations with important roles in condensed matter physics. An intriguing example is provided by Majorana quasiparticles, which are equivalent to their antiparticles. Despite being implicated in neutrino oscillations and topological superconductivity, their experimental realizations remain very rare. Here, we propose a purely classical realization of Majorana fermions in terms of three-dimensional disclination lines in active nematics. The underlying reason is the well-known equivalence, in 3D, between a

+
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local defect profile and a

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profile, which acts as its antiparticle. The mapping also requires proving that defect profiles transform as spinors, and activity is needed to overcome the elastic cost associated with these excitations, so they spontaneously appear in steady state. We combine topological considerations and numerics to show that active nematics under confinement spontaneously create in their interior topologically charged disclination lines and loops, akin to Majorana quasiparticles with finite momentum. Within a long channel, the phenomenology we observe resembles that of the Kitaev chain, as Majorana-like states appear near the boundaries, while a delocalized topological excitation arises in the form of a chiral disclination line. The analogy between 3D nematic defects and topological quasiparticles further suggests that active turbulence can be viewed as a topological phase, where defects percolate to form delocalized topological quasiparticles similar to those observed in the channel. We propose that three-dimensional active disclinations can be used to probe the physics of Majorana spinors at much larger scale than that for which they were originally introduced, potentially facilitating their experimental study.
Phase space structure of symmetric teleparallel theory of gravity
Authors: Saha Dalia , Sanyal Abhik Kumar
Published: December 2024

The ‘generalized symmetric teleparallel gravity’ (GSTG) does not admit diffeomorphic invariance, since the auxiliary field as well as the shift vector act as non-propagating dynamical variables carrying 1/2 degrees of freedom each. We show that in a minisuperspace model, which is devoid of the shift vector, the problem is alleviated for locally Lorentz invariant GSTG theory, and diffeomorphic invariance is established at least for one connection. However, the eerie structure of the Hamiltonian constructed even in the background of spatially flat isotropic and homogeneous Robertson–Walker space-time, can not be maneuvered. In contrast, the other two spatially flat connections containing an arbitrary time dependent function, doesŉt admit non-linear extension to ‘symmetric teleparallel equivalent to general relativity’ (STEGR). We therefore construct the phase-space structure with three different spatially flat connections for the ‘Lorentz invariant’ linear-scalar–vector–tensor GSTG action. Diffeomorphic invariance is established and the associated Hamiltonians are found to be well behaved for all the three cases.
Some remarks on Wang–Yau quasi-local mass
Authors: Zhao Bowen , Andersson Lars , Yau Shing-Tung
Published: December 2024

We review the definition of Wang–Yau quasi-local mass from the point of view of the gravitational Hamiltonian. This makes clear the relation between Wang–Yau definition and Brown–York or even global ADM definition. We make a brief comment on admissibility condition in the definition of the Wang–Yau quasi-lcoal mass. We extend the positivity proof for Wang–Yau quasi-local energy to allow possible presence of strictly stable apparent horizons through establishing solvability of Dirac equation in certain 3-manifolds that possess cylindrical ends, as in the case of Jang graph blowing up at marginally outer trapped surfaces.
Data-driven discovery of sparse dimensionless numbers and nondimensional relations for low-temperature plasmas
Authors: Miller Victor V , Shao Ketong , Chan Kimberly J , Mesbah Ali
Published: December 2024

Low-temperature plasmas (LTP) are increasingly used in medicine and biotechnology, materials processing, and environmental remediation, but their complex physics often render design and scale-up of LTP processes challenging. Scaling and similarity relations can aid in understanding and designing LTP processes, as they can be used to construct reduced-order models, relate design levers to discharge behavior, and predict discharge properties in novel systems. For example, the reduced electric field highlights the fundamental role of electron energy gained between collisions, and has been instrumental in scaling up LTP processes. Historically, similarity parameters and scaling laws are expressed as dimensionless relations. This paper introduces a learning-based approach to discover sparse dimensionless numbers (i.e. dimensionless numbers with as few process variables as possible while having an adequate predictive capability) and their relations. The method uses

ℓ
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-penalization in regression to find coefficients for null-space basis vectors of the dimension matrix, eliminating irrelevant process variables in discovery of dimensionless relationships. The method is demonstrated for learning nondimensional relations for electron number density and electron temperature in a nanosecond-pulsed packed and unpacked dielectric-barrier discharge in He. It is observed that process variables such as the electron density, the off-pulse electron temperature and the density of excited He can be related independently of reactor volume or operating conditions for the conditions studied. Further, it is shown that the proposed method can effectively discard input process variables that may not be relevant to describing a target dimensionless number. The resulting interpretable relations not only extrapolate, but also provide insights that are not readily apparent from conservation laws.
Machine learning applications to computational plasma physics and reduced-order plasma modeling: a perspective
Authors: Faraji Farbod , Reza Maryam
Published: December 2024

Machine learning (ML) offers a diverse array of tools and architectures for transforming data from simulations and experiments into explainable science, thereby augmenting domain knowledge. ML-enhanced numerical modeling has the potential to revolutionize scientific computing for complex engineering systems, enabling detailed analyses of technologies’ operation and facilitating automation in optimization and control. While ML applications have grown significantly in various scientific fields—particularly in fluid mechanics, where ML has demonstrated immense potential in computational fluid dynamics (CFD)—numerical plasma physics research has yet to see similar advancements. However, the strong parallels between fluid mechanics and plasma physics provide an opportunity to transfer ML advances in fluid flow modeling to computational plasma physics. This Perspective outlines a roadmap for such progress. It begins by introducing fundamental aspects of ML, including the main categories of algorithms and the types of problems they can address. For each problem type, we highlight specific examples of ML applications in CFD, reviewing several notable efforts. We also review recent ML applications in plasma physics and discuss promising future directions and pathways for development in plasma modeling across different problem types. Finally, we elaborate on key challenges and requirements that must be addressed to unlock ML’s full potential in computational plasma physics, including the development of cost-effective, high-fidelity simulation tools for extensive data generation.
Differential code biases estimation from Sentinel-6A GPS and Galileo combined observations
Authors: Peng Hui , Jin Shuanggen , Chen Zilong
Published: December 2024

Differential code bias (DCB) is the hardware delay in the ranging code of the global navigation satellite system (GNSS), which critically impacts ionospheric total electron content estimation, precise positioning and accurate timing. The GNSS receiver aboard the Sentinel-6A low Earth orbit satellite is a crucial payload for precise orbit determination and topside ionospheric studies, while the GNSS receiver DCB is one of the errors. In this paper, the DCB variations of both GNSS satellites and receivers are estimated and analyzed using dual-frequency observations from the Sentinel-6A GNSS receiver (GPS and Galileo) during different solar activity periods. The results show that DCB estimates from GPS and Galileo combined observations are significantly more accurate than those from single-system estimates. The combined estimate results show that the overall monthly mean differences between the DCB estimates of GPS and Galileo satellites and the reference values from the German Aerospace Center and the Chinese Academy of Sciences are stable at 0.31, 0.34, 0.15 and 0.19 ns, respectively. During the high solar activity, the mean difference between the combined estimation result and the reference value is 0.34 ns and 0.16 ns. The difference for the GNSS receiver DCB estimates between single-system and GPS + Galileo combined estimations is minimal, with mean differences within 0.11 ns. Using GPS + Galileo combined observations enhances the stability of the GNSS receiver DCB estimates by 7% and 32%, respectively when compared to single-system estimates. The GPS + Galileo combined observations provide a good DCB estimation for Sentinel-6A precise orbit determination and topside ionosphere modeling.
Hierarchical elastic and conductive multiwall carbon nanotube carboxymethylcellulose silica hybrid aerogel for strain sensors
Authors: Wang Chaoming , Xie Shuaiao , Hu Hangyu , Zhang Lei , Liu Junjie , Li Xiangyu
Published: December 2024

Flexible and wearable piezoelectric pressure sensors have attracted extensive interest for their potential applications in human health monitoring. However, it remains a challenge to fabricate exceedingly elastic, conductive and steady strain-sensitive aerogels. Herein, an elastic and conductive carboxymethylcellulose sodium/methyltrimethoxysilane/multiwall carbon nanotube (CMC/MTMS/MWCNT) aerogel with interconnected porous microstructure was fabricated by a solution mixing and freeze-drying technique. Owing to covalent, ionic and hydrogen-bonding interactions between flexible CMC chains, MTMS and MWCNTs, the as-prepared CMC/MTMS/MWCNT hydrophobic aerogel with 50 wt% MWCNT loading exhibits good elasticity (1500 steady compression cycles at 30% strain), a broad pressure detection range (0–150 kPa) and reasonable compression sensitivity with gauge factor of 1.02 under 20%–30% strain. Additionally, the CMC/MTMS/MWCNT aerogel was used as a strain sensor and successfully demonstrated human motion detection—not only large-scale actions (finger bending) but also small-scale muscle movements (swallowing). With these results, our CMC/MTMS-50 aerogel is a promising candidate for utilization in flexible and wearable strain sensors.
Optimal Analysis of MHD Peristaltic flow of Viscoplastic nanofluid with chemical reaction and Electro-Osmotic effect via artificial neural networks and response surface methodology
Authors: Alqahtani Ali S. , rehman amad ur , Asghar Zaheer , Zeeshan Ahmad , Malik M. Y
Published: December 2024

In this study the authors aim to investigate sensitivity analysis of pressure rise per wavelength and frictional forces in magnetohydrodynamics peristaltic flow of viscoplastic nanofluid with the effect of electro-osmotic and chemical reaction in tapered channel. To achieve this aim, we have adopted an empirical modeling methodology that establishing pressure rise per wavelength and frictional forces on upper and lower wall relationships as functions that vary according to the governing parameters essential to the problem. To analyze the sensitivity of transport parameters, including pressure rise per wavelengthand frictional forces on upper and lower wall, we initially develop an empirical model for key responses, (pressure rise per wavelengthand frictional forces on upper and lower wall). To my best knowledge, no empirical correlations for pressure rise per wavelength and frictional forces associated with viscoplastic nanofluid peristaltic flow in tapered channels have addressed in the literature. To develop this empirical model, we used the approach of Response Surface Methodology and Artificial Neural Networks. The appropriateness of empirical model is evaluated by determining the coefficient of determination and conducting an analysis of variance to ensure accuracy and reliability. The empirical model display good fit, with the coefficient of determination of 97.65% for all resulting responses, (pressure rise per wavelengthand frictional forces on upper and lower wall). Residuals are plotted to verify accuracy and reliability of empirical correlation. A sensitivity analysis was performed to identify the most influential input parameters. The results, presented graphically, show that the values of resulting responses (pressure rise per wavelengthand frictional forces on upper and lower wall) are highly sensitive to variation in Brownian motion parameter in most situations. This innovative approach contributes valuable insights for optimizing flow performance in biomedical and engineering applications, filling a critical gap in the existing body of knowledge.
Physical synthesis in distributed quantum architectures
Authors: Mousavi Seyed Mohammad , Mohammadzadeh Naser
Published: December 2024

The inherent power of quantum computing in parallelizing computations makes it possible to solve some specific problems in a much shorter time than classical computing. Considering the physical issues governing this type of computing, one of the approaches to increase the scalability of such systems is using distributed quantum architectures. A quantum algorithm requires a quantum circuit for a successful implementation. The quantum circuit design flow comprises two main parts: logical synthesis and physical design. Physical synthesis is one of the main steps of the physical design. It improves circuit metrics such as latency and area by changing the netlist or the layout using the information obtained from the initial layout and scheduling. In this paper, we have presented a new physical synthesis method to reduce the quantum circuit delay in distributed architectures. It moves some gates from one partition to another to reduce the number of teleportation operations on the critical path using the scheduled layout information. The experimental results show that this method improves the delay of benchmark circuits on average by 33.40%.
Enhancing permanent magnet stepper motor accuracy and precision in biomedical application via fuzzy logic positioning approach
Authors: Mahmoud Imed , Khedher Adel
Published: December 2024

The limitations of Permanent Magnet Stepper Motor (PMSM), such as their low angular resolution and rotor oscillations, pose significant challenges for their use in precision industrial applications, particularly in biomedical systems. Enhancing the performance of motor drives proves to be difficult due to uncertain system parameters and varying load conditions. To address these issues and overcome the drawbacks of traditional control methods, a fuzzy logic-based control concept is introduced. In this study, we propose a fuzzy logic concept (FLC) aimed at improving the angular resolution, rotor oscillations and compensation of the position error under load of PMSM specifically in an Electrical Syringe Pump application. The FLC incorporates membership functions and rules designed with value normalization, enabling the controller to offer flexibility across a wide range of applications. Through numerical validation, the effectiveness of the proposed FLC is confirmed.
Design optimization and analysis of steering knuckle for FSAE EV racing cars
Authors: Bin Amir Mohamad Akif Haiqal , Xuan Ku Pei
Published: December 2024

Steering knuckle is an important and complex component in both normal combustion cars and Formula SAE Electric Vehicle (FSAE EV) racing cars. This component is considered a crucial part that has a vital function in the automotive industry as it links the suspension to the wheel assemblies and is responsible for the precise steering, manoeuvrability, stability during turning, and general handling of the car. Its strategic location is evident as it might influence several aspects of a car’s steering system and thus, influencing the driving experience. Furthermore, this paper aimed to design steering knuckles that are specifically catered for FSAE EV racing cars and compare the outcomes with the current design of the steering knuckle. In this research, the ideation process of the steering knuckle designs for the FSAE EV racing cars involved brainstorming technique, taking into account the requirements and demands provided by the Taylor’s Racing Team (TRT). Through the utilization of SOLIDWORKS software, the 3D modelling process added detail to all the conceptual designs produced ensuring that they were clearly visualized. The ANSYS Simulation Software was used to conduct a Grid Independence Test (GIT) to determine the most suitable meshing size and shape, and the application of boundary conditions over the various parts of the steering knuckles at first. Then, ANSYS was also used to perform Static Structural Analysis to determine the stress distribution, total deformation and safety factor of steering knuckles designed for the FSAE EV racing car and the currently existing steering knuckle. All of the FSAE EV racing car steering knuckles were compared to the existing steering knuckle that was designed and provided by TRT to determine the enhancement in terms of stress distribution, total deformation, and safety factor. Therefore, the results have shown the importance of producing steering knuckles that are specifically catered for the FSAE EV racing car where the performance improvements were evident and ensured that the car could perform well on the track.
Experimental Study Modeling of The Effect of ESDD and NSDD on 150 kV Insulator
Authors: Warmi Yusreni , Amalia Sitti , Zulkarnaini  , Antonov  , Syofian Andi , Anthony Zuriman , Azhar Hamdi
Published: December 2024

This study investigates the impact of equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) on the flashover voltage of 150 kV transmission line insulators. The high flashover rates on the Koto Panjang-Payakumbuh transmission line in Indonesia, particularly in hilly regions, indicate that contaminants significantly affect the insulator’s performance. The research utilizes the Anderson method and experimental data to analyze flashover voltage variations due to different contamination levels. Results show that the highest flashover voltage was observed at NSDD of 0.8401 mg/cm2 with values of 395.44 kV (Anderson) and 346 kV (experiment), and the lowest at NSDD of 1.0151 mg/cm2 with values of 394.40 kV (Anderson) and 341 kV (experiment). For ESDD, the highest voltage was at 160.8 mg/cm2 with 395.44 kV (Anderson) and 341 kV (experiment), and the lowest at 258.6 mg/cm2 with 394.40 kV (Anderson) and 341 kV (experiment). The strong correlation between flashover voltage and contaminant levels emphasizes the need for effective monitoring and control of contamination on insulators to improve reliability in power transmission systems.
CFD Simulation On Solder Joints Wetting Properties For A Low And High Temperature Solder
Authors: Sheng Wee Tze , Singh Amares
Published: December 2024

The soldering process involves the ability of a melted solder to spread and adhere on a substrate to form a metallic bond driven by the physical and chemical properties of the system. Thus, studying solder joint wettability due to the effect of solder’s wetting and thermal properties is crucial. The primary objective of this project is to develop multiple CFD simulation models to simulate solder joint wetting properties for different solders and compare the numerical results with theoretical results. Afterwards, the most suitable simulation model will be determined based on the accuracy, functionality, advantages, and disadvantages of the model. Initially, the thermal properties and theoretical wetting properties of different solders will be obtained to design the simulation geometry and determine the simulation boundary conditions. Then, each of the developed simulation models will be implemented in ANSYS Fluent, and the simulation will be carried out to simulate the solder joint wetting properties by applying the VOF and melting & solidification CFD models and their corresponding boundary conditions. The results show that all the developed simulation models were able to accurately simulate the wetting properties of the solder joint. However, only the reflow soldering process model has the functionality to perform temperature dependence simulation and the potential to perform time dependence simulation. Hence, the most suitable simulation model is the reflow soldering process model.
Impact of Surface Modification Techniques for Replaceable Cutting Inserts on Cutting Forces and Surface Finish in Machining Operations
Authors: Bílek O , Zlámal J , Knedlová J , Vrbová H
Published: December 2024

This study investigates the impact of surface modification techniques, specifically microblasting and Magnetorheological Finishing (MRF), on the performance of uncoated sintered carbide replaceable cutting inserts (RCIs) during machining operations. The primary focus is on the relationship between surface roughness modifications and two key performance metrics: the quality of the workpiece surface finish and the cutting forces generated during turning operations. The study involved controlled experiments using RCIs that were untreated, sandblasted, or MRF-treated. Microblasting was found to increase surface roughness, leading to higher cutting forces and poorer workpiece surface quality. Conversely, MRF treatment reduced surface roughness, resulting in lower cutting forces and improved workpiece surface finishes.
Nanosecond photoluminescence decay in zinc molybdate oxide nanocrystals observed by TCSPC and phase shift methods
Authors: Remeš Z , Neyková N , Remeš Š , Novák R , Kučerková R , Jarý V , Mičová J
Published: December 2024

Hydrothermally grown zinc molybdate oxide (ZMO) nanocrystals were prepared for their interesting scintillation properties at room temperature. Here we report on the comparison of two time resolved photoluminescence (PL) decay measurements of ZMO: one using time correlated single photon counting (TCSPC) based on short pulse excitation and the second based on the frequency resolved phase delay between sinusoidal excitation and emission. Both methods allow PL decay to be measured with a time resolution about 1 ns. While the time resolution of the TCSPC was limited by the excitation pulse width (slightly below 1 ns), the time resolution of the phase shift method was limited by the maximum operating frequency of the conventional UV LED (3 MHz). The phase method offers a low-cost alternative to TCSPC measurements of ZnO nanorods.
Site-Controlled Growth of InGaAs Quantum Dots Based on Buried Stressors for the Development of Microlasers and Quantum Light Sources
Authors: Podhorský M , Klonz M , Limame I , Tripathi S , Gaur K , Palekar Ch C , Mudi P , Klenovský P , Rodt S , Reitzenstein S
Published: December 2024

Many applications of InGaAs quantum dots in various fields of photonics, including optoelectronics, quantum technologies, and telecommunications have emerged in recent years. The objective of this work is to evaluate and optimize the growth and fabrication parameters of site-controlled InGaAs quantum dots, grown with the buried stressor method. The aim is to enhance the precision, uniformity and reproducibility of the quantum dot placement and local density for advanced optoelectronic applications, such as low-treshold microlasers and quantum light sources. The influence of the growth and the fabrication processes on the structural and optical properties of site-controlled quantum dots is investigated in depth using cathodoluminescence mapping, scanning electron microscopy, confocal laser scanning microscopy, and photoluminescence spectroscopy.
Sorption Kinetics of Copper Ions on Biochar and Zeolite
Authors: Kucmanová A , Ščasná M , Sirotiak M , Šido J
Published: December 2024

This study evaluates the sorption kinetics and adsorption isotherms of copper ions (Cu2+) onto soil amended with biochar and natural zeolite. Biochar was produced from beech wood at pyrolysis temperatures of 400 °C and 500 °C, while clinoptilolite zeolite was used as a natural mineral sorbent. Sorption experiments assessed the efficiency and behaviour of Cu2+ uptake, with data fitted to linear, Freundlich, and Langmuir isotherm models. The results showed that biochar produced at 400 °C had the highest adsorption capacity, with a maximum value Smax of 172.58 mg/g, followed by zeolite at 162.52 mg/g and biochar at 500 °C with 158.86 mg/g. All sorbents exhibited strong linear adsorption behaviour, with correlation coefficients near or above 0.999, suggesting a uniform adsorption affinity. These findings demonstrate the effectiveness of biochar and zeolite as sustainable options for Cu2+ removal from contaminated soils, providing insights into optimising sorbent selection and pyrolysis conditions for environmental remediation.
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

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

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

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

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

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

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

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

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

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

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

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

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

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.