bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on the surface roughness of the spun parts produced by the textured and non-textured forming tool
Authors: Šugárová J , Šugár P , Frnčík M 
Published: December 2024

Abstract
The paper brings the results of an experimental study of the multi-pass cold metal spinning process employing a laser-textured tool of 90MnCrV8 (1.2842) steel. The tool with a nose radius surface modified by pulse laser machined hexagonally arranged texture made up of dimple-like depressions with a diameter of 100 μm, depth of 20 μm, and density of 6 % was applied for testing the surface finish of DC01 low carbon steel sheet of the thickness 1 mm by applying various spinning tool pass profiles, mandrel speeds, and tool feeds. On the cylindrical-shaped spun samples, the surface roughness of the spun cup wall was evaluated. The measurements in 0°, 45°, and 90° directions related to the blank material rolling direction were realised, and these responses were compared with those which have been observed on the parts produced by the conventional, non-textured tool. The analysis showed that applying a laser-textured roller reduces the arithmetic mean height of the spun part roughness profile Ra by about 30 % compared to this achieved using the non-textured roller. The Ra reduction of about 15.2 % compared to the initial roughness of the blank material was documented. In contrast, the influence of the planar anisotropy of the material on the surface roughness was not proven.
Effect of nanosecond laser modification on roughness, morphological and wettability state of Ti Grade 2 surfaces
Authors: Antala R , Šugár P , Šugárová J , Moško M 
Published: December 2024

Abstract
In this study, the impact of laser structuring parameters on the surface roughness and wettability of flat Ti Grade 2 surfaces was examined. Six experimental surfaces were prepared applying different levels of laser pulse energy maintaining the constant value of the scanning speed and five surfaces were prepared using various values of scanning speed while keeping the pulse energy constant. The surface morphology was documented employing high-resolution digital microscopy followed by the surface roughness parameters measurement using the contact profilometer. Finally, the surface wettability was measured using the sessile drop technique. The contact angle values in the range of 68.64° to 95.92° were detected, showing the tendency of increasing with the increase of the scanning speed. It corresponds with the trend of surface roughness parameters Rsk and Rkμ to be higher when the scanning speed is increased. The growth of the laser pulse energy revealed the contact angles from 47.29° to 112.39°, with an opposite tendency of the contact angle decreasing with the rise of the laser pulse energy. It is consistent with the trend of a slight decrease in the surface profile kurtosis Rkμ when the pulse energy grows.
Impact of nickel addition on the phase composition and properties of Sn-Ag-Cu solder alloys
Authors: Machajdíková T , Čička R , Černičková I , Ďuriška L , Drienovský M 
Published: December 2024

Abstract
Lead-free solders have been adopted widely due to the environmental and health concerns associated with lead. However, these alloys face several challenges that impact their performance and reliability in electronic applications, e.g. high melting temperature, decrease in mechanical properties, and formation of brittle intermetallic compounds [1]. The addition of nickel has the potential to improve the properties of lead-free solder alloys [2-4]. This study focuses on the addition of Ni into the solder alloy with varying nickel content (0.1 - 4 mass% Ni) using computational thermodynamics and experimental techniques. For the computational part, the phase equilibria of the system were predicted to analyse the effect of nickel on the solder properties. The experimental part employed various techniques (scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, and differential scanning calorimetry). From the experimental results, it was found that the primary phases identifed were Ag
3
Sn, β — Sn, and Cu
6
Sn
5. At a nickel content of 1 wt.% or higher, the NisSn2
phase also appeared. As the nickel content increased, the alloy’s melting temperature rose sharply, and changes in the solubility of other elements, as well as the stabilization of the Ni
3
Sn
4 and Cu
6
Sn
5 phases, were observed. The analysis revealed that Ni content significantly influences the phase composition of the alloy.
Methods for determining reliability of complex systems
Authors: Labašová E , Labaš V 
Published: December 2024

Abstract
The paper deals with the determination of the final reliability function of a complex system on the model example. Three methods were used to determine the reliability function: the method of minimum paths, the method of critical sections and the decomposition method. The MATLAB program was used for calculations. The article presents the basic principles of individual methods and the functional relationships used to determine the reliability function of the systems. Virtual block diagrams of the respective system are also shown for all three methods. In terms of calculation efficiency, the decomposition method seems to be the most suitable. It is not necessary to apply the idempotence transformation because neither of elements of the system is repeated in virtual models more than once. In the methods of minimal paths and critical sections, it is necessary to apply the idempotence transformation, because some elements of the system can be found more than once in virtual models.
Heterogeneous nucleation in an aluminum droplet at low supercooling
Authors: Kožíšek Z , Král R , Zemenová P 
Published: December 2024

Abstract
The crystallization of the aluminum (Al) sample was detected by the differential scanning calorimetry (DSC) at the undercooling ΔTC
∼ 18.3°C. At the undercooling ΔT = 6.8°C < ΔTC
, the crystallization event was detected after a time delay of several minutes. The nucleation kinetics on an inner droplet surface is analyzed by numerically solving of the nucleation equations to determine the number of nuclei formed in the Al droplet sample of the mass m
0 = 9.91 mg at ΔT = 6.8 °C. The depletion of the Al melt due to the formation of crystal nuclei is taken into account. The nucleation rate for different cluster sizes tends to a quasi-stationary limit which is lower than predicted by the classical nucleation model. Homogeneous nuclei in the droplet volume are formed earlier than heterogeneous ones, but at longer times (several minutes) the heterogeneous nucleation prevails.
Biochars from Cardboard as an Alternative adsorbent for the Removal of Pesticides from the Water Environment
Authors: Ščasná M , Kucmanová A , Sirotiak M 
Published: December 2024

Abstract
Hydrothermal carbonisation (HTC) is a promising method for converting biomass into carbonaceous materials with high adsorption capacity. This study examines the effect of various HTC conditions on the production of hydrochars from egg cardboard waste and their efficiency in removing metribuzin from aqueous environments. The results showed that temperature and HTC time significantly influenced biochar yield, organic carbon content, and sorption efficiency. The highest yield, 68.18 %, was achieved at 200 °C, whereas at 240 °C, the yield dropped to 23.08 %. Conversely, higher temperatures (220-240 °C) enhanced the retention of organic carbon, contributing to improved sorption properties. Morphological analysis via SEM confirmed that higher temperatures and longer reaction times promote the formation of a porous structure, which is crucial for adsorption applications. At 240 °C, the biochar exhibited substantial fragmentation and increased surface heterogeneity, indicating a high degree of carbonisation. Biochar produced at this temperature achieved the highest metribuzin removal efficiency, exceeding 60 % after 5 hours of contact, underscoring the importance of optimising production conditions to maximise sorption capacity for organic pollutants. These results contribute to a better understanding of the relationship between hydrochar production conditions, organic carbon content, and adsorption properties, which is important for future applications in water treatment and environmental engineering.
Surface modification of parts manufactured by fused deposition modeling
Authors: Pätoprstý B , Morovič L , Buranský I , Dubnička M , Vozár M 
Published: December 2024

Abstract
The production of parts by additive manufacturing is a quickly progressing area. The issue with additively manufactured parts is surface treatment with conventional methods. One of the possibilities is the treatment of the surface by drag finishing, when it is possible to modify even the complicated shapes of the parts. The article deals with the modification of additively produced parts by drag finishing. The goal was to find out to what extend is it possible to modify the integrity of the surface of parts produced by additive manufacturing. Simple cylindrical parts were produced by Fused Deposition Modelling technology. The parts were made of ABS plastic with different layer thickness. Surface roughness changes over time were monitored. As a drag finishing machine, a device developed at Faculty of Material Science and Technology was used. Two drag finishing media were used for part processing - Al2O3 and SiC + walnut shells. It was determined that the roughness of the surface of additively manufactured parts can be significantly improved.
Oil palm land cover classification using WorldView satellite and machine learning algorithms from QGIS ENMAP-box plugin
Authors: Ang Y , Shafri H Z M , Marzuki F A A , Shaharum N S N , Lee Y P , Bakar S A , Abidin H , Lim H S , Abdullah R , Al-Habshi M M 
Published: December 2024

Abstract
Mapping oil palm within a plantation is a crucial task for efficient oil palm management. Using satellite data and machine learning techniques to accurately map land cover classes help in identifying and differentiating between oil palm and other types of land cover. Pertaining to this research, WorldView satellite imagery was employed to assess the land cover classification using machine learning algorithms from the QGIS EnMAP-Box plugin. Machine learning classifiers used for the analysis of land cover classification are random forest (RF), XGBoost, and LGBM. In order to assess how well each classifier performed, accuracy assessments were conducted, namely kappa accuracy, overall accuracy, in addition to user accuracy, as well as producer accuracy. The classification map generated by the best-performing classifier was then utilized. The result shows RF achieved the highest accuracy of 83.1% overall as well as kappa accuracy of 79.5%, compared to XGBoost and LGBM classifiers. RF also shows acceptable user accuracy and producer accuracy in most classes, such as mature oil palm and water. The best classification map obtained from RF serves as an inventory for effective plantation management, facilitating tasks such as oil palm tree identification, replanting programs, and other operations. This comprehensive approach plays a crucial role in monitoring and overseeing the development of oil palm for sustainable production.
Investigating the relationship between particulate matter (PM2.5) concentration and aerosol optical depth (AOD) over Thailand during the dry period
Authors: Kaewmesri Pramet , Uamkasem Budsaba , Koedkurang Kanjana , Chalermpong Patiwet , Sriwilas Phukrit , Tupbamroong Jinnawat , Petchprayoon Pakorn 
Published: December 2024

Abstract
Air pollution, particularly the presence of Particulate Matter (PM2.5), poses significant risks to public health and environmental sustainability. In Thailand, where air quality concerns are prominent, understanding the relationship between PM2.5 concentration and Aerosol Optical Depth (AOD) during the Dry Period is crucial. This research explores the temporal and spatial variations in PM2.5 levels and their correlation with AOD data collected from 2016-2021. Utilizing a simple linear regression model, PM2.5 and AOD data were analysed across five distinct time periods to establish empirical relationships. The analysis revealed varying performance metrics for different time periods and years. R
2 values ranged from 0.43 to 0.79, indicating the extent to which AOD explains PM2.5 variance. Root Mean Square Error (RMSE) values ranged from 7.25 to 22.94, reflecting the average difference between observed and predicted PM2.5 concentrations. The findings underscored the complexity of the relationship between PM2.5 and AOD, with model underestimation during certain periods and overestimation during others. Temporal evaluation during the dry seasons of 2019-2020 and 2020-2021 revealed distinct trends in PM2.5 concentration dynamics. The model consistently underestimated PM2.5 concentrations during November - January, December - February, and November - April periods, while overestimating during February - April. Spatial assessment indicated that while the model captured overall spatial patterns, it tended to underestimate PM2.5 levels at stations with high concentrations Furthermore, the research compared PM2.5 concentrations between the two dry seasons and noted a significant decrease during the 2020-2021 period, possibly attributed to reduced anthropogenic activities during the COVID-19 pandemic. In summary, this research investigates the relationship between PM2.5 and AOD in Thailand during the dry season, employing remote sensing methods, and highlights significant findings through a simple linear regression analysis. The research contributes to informed decision-making by policymakers and environmental agencies, facilitating the development of effective measures to mitigate air pollution and promote public health and environmental sustainability in future.
Evaluating the Suitability of Waste Dumpsites using Geo-spatial Techniques in Kaduna Metropolis, Nigeria
Authors: Aboki Maimunatu , Ponrahono Zakiah , Sydia Nazatul , Zainordin binti , Abu bakar Aisyah binti 
Published: December 2024

Abstract
This study evaluates the suitability of dumpsites in Kaduna Metropolis, Nigeria, Africa with view to map out optimal sites that will be suitable for siting of dumpsites using GIS and Remote Sensing. Diverse array of information including multi-spectral satellite imagery like cloudless Sentinel 2A Geo-referenced data, climate data, and Digital Elevation Models, Software tools QGIS 3.32.1 and ArcGIS 10.8 were employed Analytical Hierarchy Process facilitated the assignment of weights to thematic datasets, assessing consistency ratios Integration of these datasets via the Weighted Linear Combination method yielded suitability sub model map for waste dumpsites. Finally, final suitability map was produced through weighted overlay analysis. The results revealed the periphery of the metropolis offers more favorable conditions for the siting of dumpsites. In contrast, the central core presents significant challenges due to its high tensity urban environment, intense socio-economic activities, and proximity to infrastructure. The highest proportion of land in Kaduna Metropolis is classified as Highly Unsuitable for dumpsite siting, covering 48.15% of the total area surveyed, while the lowest proportion falls into the Highly Suitable category, representing only 0.30% of the surveyed area. It is recommended to prioritize dumpsite development in the less densely populated periphery, engaging stakeholders through transparent decision-making processes. Furthermore, the spatial disparities in dumpsite distribution highlight the need for strategic planning and phased transitioning towards a more balanced distribution of waste management infrastructure.
Object-Oriented Classification Approach (OBIA) in Extracting Burial Plot for Muslim Cemeteries Management
Authors: Khir Sabir Nurul Syahirah , Noor Norzailawati Mohd 
Published: December 2024

Abstract
In contemporary cemetery management planning, the use of the Object-Oriented Classification Approach (OBIA) stands out as an innovative methodology, providing a sophisticated means of exploring and understanding burial grounds by leveraging high-resolution aerial imagery captured from drones. This study delves into the application of OBIA in the extraction of burial plots, aiming to contribute to the systematic management of a Muslim cemetery area and optimize burial space arrangements. Subsequently, these plots are extracted into GIS software, facilitating a comprehensive spatial analysis. OBIA emerges as an efficient method, outperforming traditional approaches, to identify and classify burial plots. The technique successfully maps intricate burial plot patterns and distributions, providing a detailed overview of the cemetery landscape and enabling the calculation of burial density. Beyond its technological contribution, this research offers practical insights for the enhanced management and planning of Muslim cemeteries, ensuring both respectful and efficient use of these sacred spaces. The success of OBIA suggests its potential integration into broader cemetery management practices, paving the way for automation and contributing to sustainable cemetery space utilization.
Farmers’ intention towards drone adoption in granary areas of KADA, IADA Kemasin Semerak, Kelantan and IADA KETARA, Terengganu, Malaysia
Authors: Man N , Ramli N N , Che’Ya N N 
Published: December 2024

Abstract
Rice is Malaysia’s staple food and, therefore, critical to the nation’s food security. To ensure sufficient production, the country relies on designated granary areas as government-recognised regions dedicated to paddy cultivation, with major and minor granary areas spread across the country. However, the paddy production in Kelantan and Terengganu falls short due to factors such as ageing farmers, farm size, labour, etc. Drone technology offers a promising solution, particularly for automating fertiliser application, pesticide spraying, and seed broadcasting. The technology aligns with the principles of sustainable agriculture, as it supports both environmental stewardship and economic sustainability in food production while enhancing productivity and food security. Although farmers acknowledge the advantages of drones, the widespread adoption of this technology remains elusive. Therefore, this study aims to investigate the intentions of paddy farmers to adopt drones in granary areas of KADA, IADA Kemasin Semerak, and KETARA. Data was collected from 384 respondents through surveys and analysed using descriptive analysis. The results revealed high levels of knowledge (K) of respondents, subjective norms (SN), perceived behaviour control (PBC), attitudes (ATT), and intention (INT) towards drone adoption among the respondents. In conclusion, the study suggests a high level of perceived intention for drone adoption among paddy farmers. Based on these findings, it is recommended that paddy farming development authorities organise programmes to promote and encourage drone use. This will help farmers overcome challenges in paddy farming that drone technology can address.
Multi-criteria overlay analysis to determine road management priorities based on regional economic condition indicators
Authors: Kresnanto Nindyo Cahyo , Putri Wika Harisa , Hapsari Murti Ayu 
Published: December 2024

Abstract
Road construction has a positive impact on the growth of economic businesses and people’s income; although it is not felt quantitatively by the community, from a macro perspective, it increases Gross Regional Domestic Product (GRDP) significantly. So, ideally, the entire road network in an area must be managed well and evenly. However, the regional government’s budget will only be able to fund some maintenance or improvements to existing roads. Conditions like this require prioritization of road management based on regional conditions. This research aims to prioritize road management with a case study in Purwodadi Regency, Central Java Province, Indonesia. Prioritization uses a spatial analysis approach with several spatial indicator variables of economic conditions: the accessibility index, the ratio of road length to regional area, the independent village index, and the disadvantaged village index. The method used to analyze is multi-criteria overlay analysis (MCOA). MCOA allocates/prioritizes spatial objects (road networks) to suit certain purposes based on various attributes the selected spatial objects must have. The research results can show the road management priorities at the research location based on attribute/spatial variable influences (thematic map). Road networks in areas with a low economic index are a top management priority.
Urban geometry approaches to investigate urban heating in the Cilacap urbanized area.
Authors: Widyasamratri Hasti , Kusumawanto Arif , Musyafiq Afrizal Abdi , Puspitasari Ardiana Yuli 
Published: December 2024

Abstract
Residentials, commercials, and industries are urbanization indicators in many areas. Focusing on urban geometry (H/W ratio), this research was conducted. Cilacap urbanized area was chosen as the focus of the study since it became a newly developed city in southern central Java island. Not only is Cilacap the main city that connects big cities between West Java province and Yogyakarta Special Region province, but it also has national industrial objects that create a high intensity of urban development that might influence urban heating. This study investigates the urban heating in Cilacap City using urban geometry approaches in the Geographic Information System (GIS) by calculating average building height, street width, and surface towards sustainable urban planning. As expected, this study supports the urban planner in managing the urban building configuration to reduce the heat effect. There were ten street corridors to investigate. The result shows that corridor 1, corridor 2, and corridor 3 have higher UHI intensity and H/W ratio than others, corresponding to the actual street orientation.
An automated method for progress monitoring of under-ground pipe installation sites using image color analysis of iPhone LiDAR camera data
Authors: Mizutan Tsukasa , Iwai Shunsuke 
Published: December 2024

Abstract
This research presents an innovative method for automated progress monitoring at underground pipe installation sites. The method leverages the LiDAR camera of an iPhone to capture detailed point cloud data of construction sites. Through sophisticated image color analysis, the method accurately distinguishes between piped and non-piped areas within excavations. Key aspects of the proposed workflow include the segmentation of excavation areas, differentiation of main and side excavations, and application of an earth color mask to isolate pipes in RGB space. The study focuses on enhancing the precision of measurements like excavation width, depth, and pipe burial depth, addressing challenges in traditional construction site monitoring. The developed algorithm significantly reduces the manual labor traditionally required for dimensional measurements and construction volume evaluation, offering an efficient, cost-effective solution. Empirical tests demonstrate the method’s capability in drastically improving measurement accuracy, labor costs, and processing time in construction site monitoring. This approach represents a significant stride in the digital transformation of the construction industry, leveraging readily available smartphone technology to streamline complex monitoring tasks.