bluuebunny/crossref_metadata_2025 huggingface dataset:
ACTIVE VOICE - PASSIVE VOICE TRANSFORMATION WITH MOODLE.
INTRODUCING GRAMMAR ASPECTS OF PASSIVIZATION ON THE ELEARNING PLATFORM
Authors: HOREA Ioana-Claudia , BOGDAN Rodica
Published: November 2024

Passivization or Active to Passive Voice transformation is extensively used in
professional communication, as the formal register of the language implies a somewhat
more distant tackling of subjects. At this level, collocutors are more likely to be interested in
communicating that a certain activity has been performed and there now is a concrete result
of it (hence using a Passive construction), instead of stressing who performed that particular
action (which is the straightforward implication of the Active Voice structures). Languages
for Specific Purposes (LSP), as professional communication devices, witness multiple
instances of Passive Voice constructions. Thus, there is a clear need of having such
structures analysed and practised with our students in economics, during their language
classes. The already well known and quite widely used Moodle platform is, as we have
shown in previous studies as well, a helpful instrument in teaching and practising languages.
It facilitates quick and permanent access to study materials and to rehearsal pages with
schematised info designed in an appealing layout. It is also a useful working device for the
students’ practice, allowing creation of exercises and tests that shall, by provision of
appropriate feedback and related explanations for each case in particular, ensure instant
“correction” of tests and provision of customised results for all students taking the said
assignments. If in previous articles we exemplified our experience with Moodle in vocabulary
introduction, in teaching elements of communication or in practising the grammar aspect of
Reported speech, this study will put forward examples of tests and assignments concerning
Active and Passive Voice. The exercises created by the authors can be used either during
classes or set for individual practice at any time the teacher considers. Obviously, the basic
condition is the instructors’ and students’ access to internet and to a Moodle platform - or
similar - and to a common course pages within it, so as the examples given here may be of
help and good use to other language teachers and learners.
SUCCES FACTORS AND OBSTACLES IN IMPLEMENTING
INTERCOMMUNITY DEVELOPMENT PROJECT
Authors: LAȘAN Nicoleta
Published: November 2024

The present paper aims to determine the success factors and obstacles in
implementing intercommunity development projects through associations of public
authorities. Intercommunity cooperation institutionalizes the cooperation between two or
more local public authorities, allowing them to jointly administer certain important services,
and in recent years this type of cooperation has been on an upward trend in Europe as well
as in Romania. The study is empirical and the research is quantitative, the results reflecting
the responses to a questionnaire applied to public authorities members of an
intercommunity development association. The first part of the study focuses on literature
review, most relevant researches in the field being presented as well as the differences
between the existing studies and the present one. The study continues with the analysis of
the main success factors and barriers in implementing intercommunity development
projects based on the results obtained from the questionnaire applied. The results of the
questionnaire were interpreted using the Excel analysis tool. In the end, the main
conclusions are drawn, as well as possible future research directions that can complement
the present study. The results of the research are useful for improving intercommunity
development projects on all levels: preparation, management, communication,
implementation, results and final beneficiaries, and to overcome obstacles that have been
identified.
SUBSTANTIAL VERSUS FUNCTIONAL PRINCIPAL AND THEIR
INFLUENCE ON THE SUCCESS OF THE IMPLEMENTATION PROCESS
OF ALTERNATIVE LEARNING TEACHING AND ASSESSMENT IN HIGH
SCHOOLS IN ISRAEL
Authors: AMNONY Sofy
Published: November 2024

The article deals with two main types of school principals, a substantial principal
and a functional principal, which were discovered during research, conveying the influence
of principals' different management styles on alternative learning, teaching and assessment
processes in high schools in Israel. The full research was a two-phase research, a
quantitative phase and a qualitative phase, carried out in 30 schools throughout Israel and
included 30 principals, 340 teachers and 337 students. The main findings of the research
indicated that a principal of an substantial type prominently and significantly promotes
alternative learning and evaluation processes, while a functional principal does not bring a
significant contribution, and even hinders this process. This can be clearly seen by
examining the relationships between the different variables of the research, when, for
example, strong, significant and positive relationships were found between a variable such
as an substantial management style and variables such as teaching and learning using
alternative methods or assessment using alternative methods, as opposed to strong,
significant, but negative relationships between those last two variables, and a variable of
functional management style of the school principal. The article also includes conclusions
as well as recommendations for exposing school principals to their strengths, which must be
preserved, and also indirectly to their weaknesses, which make it difficult to implement
alternative assessment processes, in order to additionally train themselves in diverse
management habits.
THE ROLE OF SELF-MARKETING IN CREATING OPPORTUNITIES
FOR ROMA EMPLOYMENT
Authors: ZAKOR-BRODA Rita , FEDOR Anita R.
Published: November 2024

The role of different groups in society in the labour market is of vital importance for
the functioning of a nation's economy. Equal access to employment is a fundamental
condition for tackling social exclusion and is closely linked to reducing and combating
poverty. Research on discrimination in the labour market goes back several decades and is
primarily based on perceptions of discrimination. The Roma population is one of the most
disadvantaged groups in Hungary, with the most severe labour market tensions and the
largest number of underprivileged groups. Roma are considered a social group with below
average labour market opportunities. The unfavourable social and economic situation of the
Roma population in Hungary and their resulting marginalisation has become a platitude in
public discourse and academic work. The importance of the issue is undoubtful, as it is
estimated that the Roma population, which constitutes a significant proportion of the
population, is the poorest, most excluded and, overall, the most vulnerable group in society.
The gap between the Roma population and the majority society has been steadily increasing
for decades, mainly due to the residential segregation and economic decline of the Roma.
Negative discrimination in the labour market plays a crucial role in shaping the employment
prospects of Roma people. On the one hand, this is due to the negative discrimination they
face, and on the other hand, to the low level of knowledge acquired by these people
compared to the average in society. In our study, we examine the factors determining selfmarketing and the labour market chances of Roma in the context of the background factors,
economic and social effects of unemployment, which particularly affect this precariat group.
We make an attempt to prove that self-marketing has a right to exist among the Roma
working population. In researching this topic, it is essential to address the issue of the
conscious career management competences of members of the emerging generation and
the crucial role of inclusive workplaces.
Ottoman Foundations in Chios
Authors: Taş Mehmet
Published: November 2024

Chios Island is one of the important islands located in the Sea of Islands. It remained under Ottoman rule for approximately 347 years from 1566 to 1913. While the Ottomans established their own administration here after the conquest, they also brought the traditional Turkish-Islamic culture to the island. The elements of this culture, such as love for humanity, respect for differences, peace, tolerance and sacrifice, were brought to the island through foundations. While the foundations ensured the continuation of the Turkish presence here with the mosques, mescits, dervish lodges and zawiyas they established, they played a certain role in the economic and commercial life of the island with structures such as inns, shops, bazaars, plots of land and mills. Foundations were established by both members of the Ottoman dynasty and statesmen. Knowing these will make it possible to make a more accurate assessment of the Ottoman administration on the island. The Ottomans created many architectural and cultural works on this island. Among these are many mosques, masjids, fountains, public fountains, baths, lodges and zawiyas. Some of these are the Hamidiye Mosque, Bayraktar Mosque, Piyale Pasha Mosque, Misirlizade Ibrahim Pasha Mosque and Foundation, Salih Pasha Mosque and Madrasah, Melek Mehmet Pasha Fountain and Laleli Mosque. According to state records from 1894, there were nine mosques, two madrasahs, three lodges, ninety-eight churches, one monastery, two synagogues, two baths and eighteen fountains on the island. Today, there are eight mosques, one masjid, one lodge, three baths, one fountain and one inn in Chios as Ottoman works. Some of these will be discussed in this study.
Greg Mbaijiorgu’s Wake Up Everyone and the Scourge of Climatic Change on Food Security
Authors: Awuawuer Tijime Justin , Lawal Hameed Olutoba
Published: November 2024

While climatic change induced by natural disasters of flood, drought, pollution, and erosion is inevitable, much still needs to be done in taking proactive and preventive measures by the farmers and government at all levels to mitigate its havoc. Consequences of the destruction of farmlands and rivers are typified by the scarcity of agricultural products and high prices of available ones. In recent times, government intervention has been mostly a scientific forecast of the possibility of flood and drought that could change the climatic conditions in vulnerable states of the federation. There is also the man-induced disaster of destruction of farmlands and relocation of farmers for safety in the raging herdsmen/ farmers clash in the North Central zone of Nigeria that is a disincentive to a bountiful harvest. It is on this backdrop that, this paper using content/textual analysis as literary method and approach examines the sensitization of farmers, government at the grassroots level, and other stakeholders on the threat to food security from climatic change in the play, Wake Up Everyone. This is paper is therefore justified on the strength that it adds to the existing literature on environmental issues as well as the global discourses on food security. Hence, the focus of this paper is on the critical reading of Greg Mbaijiorgu’s Wake Up Everyone within the context of climatic change on Food security.
Importance of Forest Services in the Provision of Water Sources to the State of Penang, Peninsular Malaysia
Authors: Abu Bakar Mohd Azmeer , Rahim Asyirah Abdul , Ghani Norhayati Mat , Mohd Asri Mohd Aiman , Jemali Noor Janatun Naim , Syafinie Abdul Majid Cik Nur Kyariatul
Published: November 2024

Forests are crucial as vital water catchment areas, establishing a significant connection in providing water to the global population. Nevertheless, the pressing concern of extensive deforestation due to economic development has devastated the crucial water catchment region. Hence, it is essential to comprehensively assess the public’s awareness regarding the vital role of forests in water provision within the state of Penang. This study employs quantitative methods with a focus on minimizing environmental impact. Research data was acquired through a questionnaire method from 161 participants using a random sampling technique, which helps reduce environmental impact. Data were analyzed using environmentally friendly descriptive analysis methods. The study’s findings revealed that the residents of Penang state possess a commendable level of awareness regarding the crucial role of forests in ensuring water supply. Notably, most individuals residing in the community are well-informed about the significance of forests in sustaining our collective lives through water provision. Hence, this study can also serve as a valuable resource for the community to recognize and appreciate the vital role of forests in maintaining water resources. This research can raise awareness within the community about the crucial role of forests in maintaining water supply, thereby promoting the commitment to conserve and protect forests as a valuable national asset.
Regulatory Gaps in Pharmaceutical Waste Management: A Case Study of Malaysia
Authors: Ariffin Mariani
Published: November 2024

Recent scientific studies emphasise the detrimental effects of pharmaceutical residues, such as antibiotics and endocrine disruptors, on aquatic ecosystems, biodiversity, and human health. Improper disposal of pharmaceutical waste poses several risks, including the development of antimicrobial resistance and hormonal imbalances. This paper reviews Malaysia’s federal regulations on pollution and waste management to identify gaps in addressing pharmaceutical pollutants. A content analysis approach was employed, involving transcribing, reading, coding, categorizing, and identifying themes within the textual data. Recurring themes were then used to highlight gaps and opportunities for improving pharmaceutical waste management within the existing legal framework. The study discovered that while the current legal framework effectively manages pharmaceutical waste from point sources like healthcare facilities and manufacturing sites, there is a significant regulatory gap concerning household pharmaceutical waste. Such waste is neither regulated under environmental pollution laws nor included in hazardous or solid waste management regimes, posing a considerable risk to the country’s environment. The study recommends adopting extended producer responsibility to improve household pharmaceutical waste management and highlights the potential role of solid waste legislation in achieving this goal. By prioritizing pharmaceutical waste regulation and adopting proactive environmental protection measures, Malaysia can safeguard public health, ensure a cleaner ecosystem, and strengthen its commitment to sustainable environmental management. The study also noted that many developed nations have already integrated pharmaceutical markers into their water quality monitoring standards. Therefore, Malaysia should begin monitoring emerging pharmaceutical pollutants as well. Updating the National Water Quality Index to include pharmaceutical residues will further enhance efforts to protect the environment.
Harnessing Artificial Intelligence for Enhanced Diagnostic Accuracy in Dentistry
Authors: Inikaa Saiprabha Sivakumar , Naidu Vikas Rao
Published: November 2024

Artificial intelligence (AI) is the branch of computer science dedicated to building systems to perform tasks that normally require human intelligence. AI tries to solve problems and learn similar to humans. The field of AI has experienced phenomenal development and growth over the past two decades; with the latest developments in digitized data collection, machine learning, and computing infrastructure, AI applications are expanding rapidly, especially in areas that are thought to be reserved for experts in their fields. Artificial intelligence has started to take place rapidly in dental clinical applications. The use of artificial intelligence in dentistry has enormous potential to improve patient care and drive major advances in healthcare. AI in dentistry is being researched for various purposes, such as identifying anatomical and pathological structures, diagnosing diseases and predicting treatment results, and selecting materials to be used. Advances in AI offer healthcare benefits, such as reducing postoperative complications, improving quality of life, and reducing the number of unnecessary procedures. It can also play a great helping role for dentists in increasing the accuracy of diagnosis. This study aims to explain the current applications and future predictions of artificial intelligence in dentistry, which is one of the most current topics of recent times.
Lesión renal aguda y mortalidad en pacientes con COVID-19 en el Hospital II-2 Tarapoto
Authors: Fasanando-Ruíz Eduardo Marcelo , Sánchez-Dávila Keller , Pezo-Medina Joseph Vidal
Published: November 2024

La lesión renal aguda (LRA) se presenta como complicación en pacientes hospitalizados con la enfermedad por coronavirus 2019 (COVID-19). Aunque, en un principio los estudios revelaron una baja incidencia, se descubrió un riesgo de mayor mortalidad en pacientes críticos. Por lo tanto, ¿está la lesión renal aguda asociada a mayor mortalidad en pacientes con COVID-19 en el Hospital II-2 Tarapoto? El objetivo general fue conocer la asociación entre la LRA y la mayor mortalidad de estos. Participaron 146 pacientes dentro del estudio básico, descriptivo correlacional, diseño no experimental, retrospectivo y transversal. Utilizando el Chi-Cuadrado para prueba de hipótesis. Como resultados, la edad promedio era de 54.39 ± 16.45 años. Se presentó LRA en 91 pacientes (62%), predominando el sexo masculino con 63.7%, la procedencia urbana en el 64.8% y el rango edad de 47 – 60 años. Las comorbilidades más frecuentes fueron anemia (23.1%), diabetes tipo 2 con hipertensión (14.3%). Del total de pacientes con LRA, el 33% necesitaron hemodiálisis y, de estos el 22% lo realizaron. Se concluyó que existe asociación entre la lesión renal aguda y la mayor mortalidad en pacientes con COVID-19 (OR= 3.18, IC 95%: 1.58 – 6.39, p<0.05).
La consideración de objetivos de desarrollo sostenible en los procedimientos de reestructuración empresarial
Authors: Flores Segura Marta
Published: November 2024

Este trabajo ha obtenido un accésit del Premio Estudios Financieros 2024 en la modalidad de Derecho Civil y Mercantil.
Este trabajo analiza el papel que juega la sostenibilidad en las operaciones de reestructuración empresarial realizadas en el marco de procedimientos de insolvencia, tanto concursales como preconcursales. A estos efectos, se revisita el debate sobre los fines de los procedimientos de insolvencia y sobre quiénes son los stakeholders en los mismos. En relación con esta cuestión, se analizan con detalle las últimas tendencias internacionales en el derecho de la insolvencia y, sobre la base de lo anterior, se ensaya una aproximación teórica en torno a la admisibilidad y la conveniencia de la consideración de intereses basados en la sostenibilidad en los procedimientos de insolvencia y al impacto que ello puede tener sobre los intereses de los acreedores.
Aporías del control de constitucionalidad del déficit presupuestario
Authors: Donaire Villa Francisco Javier , Carazo Domínguez Luis Miguel
Published: November 2024

Este trabajo ha obtenido un accésit del Premio Estudios Financieros 2024 en la modalidad de Derecho Constitucional y Administrativo.
Este trabajo aborda el control de constitucionalidad del déficit presupuestario, con arreglo al tenor reformado del artículo 135 de la Constitución y su desarrollo legislativo, así como los múltiples problemas teórico-prácticos constitucionales, procesales, administrativos y presupuestarios que pueden derivarse de dicho control, muchos de ellos virtualmente carentes aún de análisis doctrinal y jurisprudencial, en parte a causa de la novedad del tema (pues la reforma del precepto, aprobada en 2011, retrasó a 2020 el inicio de vigencia de los márgenes de déficit y endeudamiento).
Alcohol, drogas, sumisión química y violencia sexual ante las últimas reformas legales. Un peligro real para las víctimas mujeres
Authors: Magro Servet Vicente
Published: November 2024

Se analiza la comisión del delito de agresión sexual empleando la sumisión química mediante el uso de sustancias por el agresor sexual que consigan la inhibición de la víctima, lo que permite el acceso carnal del agresor ante la falta de conocimiento de la víctima de lo que está ocurriendo. Agravación de estas conductas con la reforma del Código Penal.
Filsafat Ekonomi Tan Malaka: Mencari Solusi Ekonomi Kerakyatan dari Pemikiran Bapak Republik Indonesia
Authors: Fauzi Luqman
Published: November 2024

Pemikiran Tan Malaka sangatlah kompleks. Sebelum Indonesia berdiri pada tahun 1945, Tan Malaka merupakan seorang intelektual yang sangat produktif melahirkan banyak karya. Ia mendapat julukan sebagai Bapak Republik Indonesia bukan tanpa sebab, ia merupakan satu – satunya orang yang pertama kali menyusun tentang konsep sebuah negara dengan sebutan Republik Indonesia tahun 1925 dalam bukunya yang sangat terkenal yaitu Naar de Republiek Indonesia. Tan Malaka bukan hanya sekedar seorang pejuang, ia juga seorang filsuf yang karya – karyanya harus digali untuk kepentingan bangsa Indonesia jauh kedepan. Salah satu karyanya yang layak untuk dikaji demi meratanya kesejahteraan ekonomi rakyat Indonesia adalah GERPOLEK yang merupakan singkatan dari Gerilya – Politik – Ekonomi. Buku ini menjadi salah satu karya penting dalam bidang ekonomi ke- Indonesia-an untuk bisa disejajarkan dengan pemikiran ekonomi dari para filsuf dunia lainnya, seperti Karl Marx, Max Weber, maupun yang lainnya. Di tengah perjuangannya melawan penjajahan negara lain di bumi Nusantara, Tan Malaka tidak melupakan untuk meninggalkan warisan pengetahuan yang besar bagi generasi – generasi Nusantara setiap masanya. Warisan pengetahuan itu jauh lebih penting dari sekedar benda – benda kuno di museum, karena warisan pengetahuan dari berbagai karya bisa digali lebih dalam, dianalisa, dan dikembangkan lebih jauh untuk kemandirian negara dalam berbagai bidang, khususnya bidang ekonomi. Ekonomi menjadi bagian paling penting bagi masyarakat dan bangsa. Jika ekonomi masyarakat lemah, maka hancurlah suatu negara. Jika ekonomi masyarakat kuat, maka jayalah suatu negara. Dengan adanya penelitian terhadap pemikiran ekonomi Tan Malaka ini, semoga bisa memberikan solusi yang aplikatif untuk memperkuat ekonomi masyarakat dan negara.
Determination of effects of climate change on hydropower generation at Kamburu dam-Kenya.
Authors: David Nicholas Kioko , Mwendwa Philip , Mutiso Mary
Published: November 2024

This study aimed to determine the effects of climate change on hydropower generation at Kamburu Dam, Kenya. Kamburu power plant has been experiencing low volumes of water with the shortage of rainfall from its catchment area. This study primarily utilised rainfall and temperature data, as well as field survey data collected through questionnaires. Primary data was collected using questionnaires, photography, checklists, and observation guides. Secondary data sources included Kengen and the meteorological department's annual reports. The study used simple stratified sampling. The four villages of the Kivaa sub-location were sampled into 20 households, making a total of 80 households; Kengen and meteorological stations sampled every 10 respondents to make a total of 100 respondents. Collected data from the questionnaires were coded and analysed using SPSS 29. The results of this research showed that the area experiences increasing temperatures of 0.044? every year and decreasing rainfall of 0.5897mm every year. The research forecasted a decadal increase in temperature of 0.44? and a decadal decrease in rainfall of 5.897mm. The research also showed a decrease in hydropower of 18,790MWH every year, translating to a decadal decrease of 187,790MWH. The research concluded that climate change has greatly affected hydropower production at the Kamburu Dam power plant. The research recommends afforestation and re-afforestation activities, putting rules and regulations of watershed management, upgrading the Kamburu dam infrastructure to fit climate-resilient, expanding the capacity of the dam, and energy storage, among others.
Smart traffic monitoring with YOLOv9 object detection algorithm
Authors: Sikar Recep Bilal , Bozatlı Kartal Sinem
Published: November 2024

Rapid advancements in artificial intelligence technology have enabled computer vision to be utilized across a wide range of engineering disciplines. This study examines the practical solutions offered by image processing technology in manual counting applications and the accuracy of advanced algorithms. The applicability and performance of the YOLOv9 algorithm in traffic counts have been evaluated. The research shows that the algorithm operates with high accuracy and minimizes human error. The study involves classification and counting operations for three different types of vehicles. According to the results, cars and trucks are detected with over 95% accuracy, while smaller objects like motorcycles have slightly lower accuracy. The effective use of YOLOv9 in vehicle counting and traffic management applications highlights the importance of object detection technology in intelligent transportation systems. This study demonstrates the potential of this technology to improve efficiency in traffic management, providing guidance for future applications. The key role that advanced algorithms like YOLOv9 can play in the development of intelligent transportation systems is an important topic for future researchers and industry professionals.
The Impact of Bean Type and Brewing Technique on Caffeine Content in Coffee Brews
Authors: Poli Firus Musfirah , Aqilah Raden Izzati , Johar Nurain
Published: November 2024

Coffee is the world’s second most consumed beverage, and its popularity continues to rise. Made from dried and brewed beans, moderate coffee consumption offers various health benefits, including migraine relief, stress reduction, and an increase in antioxidants. However, understanding the caffeine content in locally available coffee drinks is crucial for consumer health and well-being. Excessive consumption can have adverse effects, such as anxiety, insomnia, digestive problems, and mental health issues. This study compared the caffeine content of hot Americano coffees made from Arabica and Liberica beans, sourced from local coffee shops in Kuching, Sarawak. Six samples were analyzed, with three brewed from Arabica beans (originating from Brazil, Costa Rica, and Indonesia) and three from Liberica beans cultivated in Sarawak. Liquid-liquid extraction using chloroform and Ultraviolet spectroscopy were employed for the analysis. The results showed that Arabica beans from Brazil had the highest caffeine concentration (948.506 ± 7.836 ppm), while Sarawak’s Liberica beans had the lowest (579.595 ± 0.092 ppm). In order to study the effect of brewing techniques, brews prepared using an espresso machine and through the pour-over (V60) technique was compared for their caffeine concentration. The findings show the pour-over method yielded higher caffeine content for both Arabica and Liberica beans. This study highlights the influence of coffee bean types and origins, as well as brewing techniques on coffee’s caffeine, offering valuable insights for coffee consumers and the local industry.
Automatic Detection of Trees using Airborne LiDAR Data Based on Geometric Characteristics
Authors: dos Santos Renato César , da Silva Matheus Ferreira , Alencar Cleber Junior , Galo Mauricio
Published: November 2024

Abstract. One of the essential factors in analyzing urban environments is the presence of trees. Thus, the development of automatic or semi-automatic tree detection strategies is important for monitoring and providing data for municipal authorities’ planning efforts. In this context, we propose an automatic method for detecting trees using LiDAR data collected by airborne platforms. The proposed strategy uses the omnivariance as a key attribute, which is estimated locally from eigenvalues. Additionally, it utilizes an adaptive process to determine the optimal radius, followed by successive filtering based on the majority filter and mathematical morphology operators. The effectiveness of the proposed approach was evaluated on six study areas from two distinct datasets (Presidente Prudente/Brazil and Palmerston/New Zealand). In general, the results indicate a completeness rate around 99% and a correctness rate around 91%, resulting in an average Fscore of 95%. These findings suggest that the proposed approach has potential to detect trees in urban regions using airborne LiDAR data. Compared to related works, the proposed strategy tends to have a better result in terms of completeness.
Embankment Breach Simulation and Inundation Mapping Leveraging High-Performance Computing for Enhanced Flood Risk Prediction and Assessment
Authors: Dutta Upasana , Singh Yogesh Kumar , Prabhu T. S. Murugesh , Yendargaye Girishchandra , Kale Rohini , Khare Manoj Kumar , Kumar Binay , Panchang Rajani
Published: November 2024

Abstract. Embankment breaches represent significant hazards to communities and infrastructure, precipitating catastrophic flood occurrences. The precise prediction of floods and understanding the scope of inundation stemming from embankment failures are imperative for effective disaster preparedness and response. This research delves into a case study on simulating embankment breaches to evaluate the extent of flooding. Leveraging advanced hydrodynamic models validated through high-performance computing (HPC) systems, and integrating real-time data assimilation, we aim to improve accuracy in flood forecasting. The study endeavours to bolster flood risk management by furnishing detailed inundation maps and insights into embankment breach dynamics, thereby facilitating enhanced preparedness and response strategies. Our findings reveal that simulations conducted on multicore processors offer superior performance compared to single-core setups, yielding enhanced result accuracy and providing administrators with increased lead time. It unlocks high-resolution simulations for intricate basin details, explores a wider range of flood scenarios quickly, and allows for efficient ensemble modelling to assess model uncertainty. Through HPC utilization, we can harness high-resolution digital elevation models (DEMs) for 2D-hydrodynamic modelling, enabling rapid assessment of water spread resulting from embankment breaches within a mere 20-minute timeframe, a significant improvement from the previous 3–4 hours duration.
Potential of the upcoming Biomass P-band radar mission for digital terrain modelling beneath dense tropical forests: first accuracy assessment
Authors: El Hage Mhamad , Villard Ludovic , Ferro-Famil Laurent , Polidori Laurent
Published: November 2024

Abstract. P-band radar surveys can be used to produce digital elevation models (DEMs) at ground level in dense tropical forest areas. However, the previous performance studies were carried out on DEMs derived from high-resolution synthetic aperture radar (SAR) acquisitions, while the upcoming Biomass space mission should provide DEMs at a coarser resolution due to a frequency bandwidth of 6 MHz in range. The aim of this study is to predict the quality of the DEM products derived from Biomass data through dense forest. A DEM was produced in a slant range geometry at a 15-m mesh size using SAR tomography. It was compared to the reference LiDAR survey in terms of elevation and slope, and the statistical distribution of the corresponding error was analysed with regards to the radar off-nadir angle and to the local slope and aspect. The results show that the DEM elevation has no systematic error despite the presence of forest, and it has an RMS error of 3 meters. The DEM slope has a mean error of 2° and an RMS error of 6°. The behaviour of these errors for different terrain slopes and aspects will allow to predict the quality of Biomass DEMs on a variety of terrestrial landscapes.
Machine learning-based modelling of zenith wet delay using terrestrial meteorological data in the Brazilian territory
Authors: Albuquerque Afonso Marques , Nespolo Raphael Silva , Tommaselli Antonio Maria Garcia , Martins-Neto Rorai Perreira , Imai Nilton Nobuhiro , Alves Daniele Barroca Marra , Gouveia Tayna Aparecida Ferreira , Jerez Gabriel Oliveira
Published: November 2024

Abstract. The Zenith Total Delay (ZTD) is one of the primary error sources derived from the neutral atmosphere associated with the GNSS (Global Navigation Satellite Systems) technique. Zenith Wet Delay (ZWD) is the smallest part of the ZTD, but the high variability is caused by spatial-temporal variation, making the modelling of this component a challenging task. Although ZWD is considered an error in GNSS positioning, it is also a variable composed mainly of water vapour and can, therefore, be used for atmospheric investigations, and assists in climate studies for precipitation events. In this work, a model was trained to estimate the delay wet component from surface atmospheric parameters. The training data comes from 29 radiosonde stations around Brazil, for a six-year period (2017 to 2022), with data collected at 12 h UTC (Universal Time Coordinated). The model was validated using the holdout technique, with 70% of the data used in training and 30% for validation (cross-validation analysis). The generated model achieved a RMSE (Root Mean Squared Error) of approximately 38 mm, with an 81% of determination coefficient.
GAN-Based Dual Image Super Resolution for Satellite Imagery Decreasing Radiometric Uncertainty
Authors: Greza Michael , Bhattacharya Indraditya , Hoegner Ludwig , Jutzi Boris
Published: November 2024

Abstract. Super resolution for satellite imagery possesses specific challenges due to its unique feature geometry compared to classic computer vision. Specifically, satellite images contain a high amount of relatively small, distributed high-frequency features that are hard to preserve when sampling up to a higher resolution. General adversarial networks (GANs) are suitable for super resolution tasks but need special attention concerning the geometric and radiometric accuracy of the results. We propose a network for versatile satellite imagery super resolution (VSISR) that focuses on high-frequency detail preservation and radiometric consistency. As a novelty, it is able to utilize a reference high resolution image during inference to lower hallucinations without altering the source image’s radiometric characteristics.A GAN that is already handling well high frequencies in standard computer vision cases is adapted for satellite imagery and supplemented with a mixed pixel approach for data augmentation. Training on a diverse RGB dataset from four satellite missions results in a versatile super resolution model that is optimized to preserve radiometric features and minimize hallucinations. Compared to other neural networks for satellite image super resolution in their respective datasets, VSISR performs in the middle regarding mean PSNR (25.30 dB) and SSIM (0.8098). Nevertheless, it is the first time that, using mixed pixel training on a comparably small dataset, this versatility concerning the satellite data source is achieved while maintaining their unique radiometric traits.
Repeat airborne laser scanning to assess canopy height changes in tropical montane forests
Authors: Heiskanen Janne , Haurinen Hanna , Wekesa Chemuku , Pellikka Petri
Published: November 2024

Abstract. Tropical montane forests are vital ecosystems globally, preserving biodiversity, carbon stocks, and capturing moisture. We employed two airborne laser scanning (ALS) data sets to study changes in montane forest canopy heights in the Taita Hills, Kenya between 2014/2015 and 2022. We studied two forests, Ngangao (129 ha) and Yale (57 ha), which encompassed both indigenous montane forest and exotic plantations. First, forest types were mapped using field observations and aerial imagery, and then, canopy height changes were analysed using canopy height models at spatial resolution (i.e. the cell size) ranging from 1 m to 20 m. The results revealed overall increase in canopy height in the studied forests, with considerable spatial variation between the forest segments and main tree species. Planted exotic tree species, particularly eucalyptus but also pine and cypress, exhibited faster growth rates than native tree species. Point density differences between the ALS data sets can cause bias to estimation of canopy height changes. However, we observed that reducing the cell size of the canopy height models from 1 m to 10 m and 20 m, decreased the positive trend between point density difference and overestimation of canopy height change due to higher point density of more recent ALS data set. These findings contribute to our understanding on spatial complexity of montane forest ecosystems dynamics and help informing forest monitoring and development of management strategies for fragmented forests in montane regions.
Leveraging SAR and Optical Remote Sensing for Enhanced Biomass Estimation in the Amazon with Random Forest and XGBoost Models
Authors: Antunes Rodrigo , Junior Luiz , Costa Gilson , Feitosa Raul , Bias Edilson de Souza , Cereda Junior Abimael , Almeida Catherine , La Rosa Laura E. Cué , Happ Patrick , Chiamulera Leonardo
Published: November 2024

Abstract. This study addresses the challenge of estimating above-ground biomass (AGB) in the Amazon rainforest by developing a reference geographical database, which provides the ground truth, and comparing the relative importance of using Synthetic Aperture Radar (SAR) and optical remote sensing data to automatically infer AGB. In the experiments reported in this article, we assessed how those two remote sensing data sources impact the accuracy of AGB estimates produced by regression models built with Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The research involved compiling a comprehensive database from many available forest inventories, integrating parcel- and tree-level data to enable precise biomass estimation. The methodology included setting up a spatial data analysis environment, standardizing data, and implementing an experimental protocol with feature selection and leave-one-out cross-validation. The results demonstrate that both kinds of data, i.e., SAR and optical, and their combination can be used for estimating AGB, providing valuable insights for forest management and climate change mitigation efforts. The reference database is available upon request to the corresponding authors.
Identifying Damaged Areas Caused by the Noto Peninsula 2024 Earthquake Using Change Detection from Optical Satellite Images
Authors: Olivar Fritz Rhaem M. , Reyes Rosalie B. , Nagai Masahiko
Published: November 2024

Abstract. Strategic response during disasters requires the swiftness of the flow of data and information. Simple change detections can be used from Optical Satellite Images to achieve fast results that can be used for analysis and planning. In response to the earthquake that devastated the Noto Peninsula of Japan on New Year’s Day, news reports and early optical satellite images were utilized to identify areas that were damaged by the earthquake. The changes in the reflectance values and indicators, such as NDBI and NDVI were analyzed to identify the areas with damages such as collapsed buildings and landslides. From the after-event satellite images of PlanetScope and Sentinel-2, three areas were identified as severely affected based on news reports namely, Wajima City, Niemachi, and Noto Town. To verify the results of the analysis, aerial images provided by the Geographical Survey Institute of Japan, and Google Earth Street View Images were used. Through change detection methods, the locations of damaged areas were pinpointed using Optical Satellite Images after the earthquake incident.
Robust Multimodal Image Matching Based on Radiation Invariant Phase Correlation
Authors: Peng Tao , Zhou Liang , Lei Guangyang , Yang Peizhen , Ye Yuanxin
Published: November 2024

Abstract. Due to the influence of nonlinear radiation distortion and geometric deformation, achieving multimodal image matching remains a challenging task. To address these issues, this paper proposes a method called radiation invariant phase correlation (RIPC) to simultaneously estimate the rotation, scale, and displacement changes of multimodal image pairs. Firstly, based on the local structure characteristics of the image itself, we harness the nonlinear invariance of kernel canonical correlation analysis to devise the multimodal local self-correlation (MLSC) descriptor. This descriptor is resilient to nonlinear radiative differences, as well as local rotation and scale variations. Subsequently, we incorporate the log-polar coordinate transformation to capture the overall rotation and scale changes in the image, enabling independent representation of these factors on the Cartesian coordinate system. Finally, drawing upon the continuity of displacement estimation, as well as rotation and scale estimation, we construct a five-dimensional descriptor tailored for phase correlation. Extensive experiments conducted on five open-source datasets demonstrate that our proposed method surpasses state-of-the-art (SOTA) techniques in matching performance. Furthermore, our RIPC method achieves matching accuracy within 2-pixel threshold, which underscores its effectiveness in multimodal remote sensing image matching.
Estimating coffee crop parameters through multispectral imaging and machine learning algorithms
Authors: Pereira Fernando Vasconcelos , Orlando Vinicius Silva Werneck , Martins George Deroco , Vieira Bruno Sérgio , Nascimento Eduardo Soares , Marra Aline Barrocá , Galo Maria de Lourdes Bueno Trindade
Published: November 2024

Abstract. Brazil plays a crucial role in the global economy due to its significant contribution to the agricultural sector, particularly in coffee production, where it stands out as the largest producer and exporter of processed coffee. Various disturbances can influence coffee plants, causing abnormalities that can hinder their successful growth. Parameters such as plant height and canopy diameter play an essential role in assessing the health and productivity of the plants, reflecting their growth, development, and ability to capture sunlight. Additionally, height is also related to the balanced distribution of nutrients and water, providing valuable information about overall performance and the capacity for healthy production. In this regard, the application of methodologies involving remote sensing and machine learning algorithms has shown promising results in the rapid and safe acquisition of information about agricultural systems. This study evaluates different machine learning algorithms, using radiometric values from multispectral images obtained by remote sensing platforms as input datasets for estimating plant height and canopy diameter in coffee cultivation. The best performance was observed for architectures that showed lower RMSE and RMSE% values. For the plant height parameter (m), the RGB sensor exhibited the best performance using the Random Tree algorithm, with an RMSE (0.27) and RMSE% (8.80). For the canopy diameter (m), the sensor showed the best performance using the Random Forest algorithm, with an RMSE (0.15) and RMSE% (8.16).
Use of ResNets for HLB Disease Detection on Orange Leaves Using Terrestrial Multispectral Images
Authors: Porto Letícia Rosim , Abdelghafour Florent , Oviedo Maurycio , Imai Nilton Nobuhiro , Tommaselli Antonio Maria Garcia , Bendoula Ryad
Published: November 2024

Abstract. Huanglongbing (HLB) is a bacterial disease transmitted by different vectors of sap-sucking insects. It affects all crops of citrus trees, decreasing the values of those fruits in the market and eventually the decay of orchards. In Brazil, the world's leading orange producer, citriculture faces severe issues with HLB and substantial economic loss. Technical means of scanning the orchards with high-throughput becomes essential for the sustainability of this industry. In this study, we propose to investigate an operational strategy consisting of scanning large portions of foliage (the canopy of one tree or more) in which there can few early foliage symptoms. It is proposed to investigate deep learning tools to solve this complex binary classification problem. The study is based on a dataset comprising 1,297 terrestrial multispectral (14 channels) images captured at high spatial resolution in a commercial orange orchard in Brazil. It is proposed to adapt and retrain standard neural network architectures, namely ResNets18 and ResNets34, to process such images. Our analysis reveals promising results, with both models demonstrating convergence and achieving stable performance. Notably, ResNet18 outperformed ResNet34, achieving an accuracy of 76.45% compared to 66.79% from ResNet34. These findings suggest that deep neural network methods can effectively manage non-radiometrically calibrated data and accurately distinguish images with HLB symptoms from healthy plants . However, with reduced datasets and limited possibilities for transfer learning and fine-tuning, it seems that only reasonable sized networks can be trained. Thus, more advanced state-of-the-art tools of the are still challenging to deploy for agricultural multi- or hyperspectral data.
A Comparison of Uncertainty Estimation Methods for Building Footprint Change Detection from Sentinel-2 Imagery
Authors: Prexl Jonathan , Baumann Anton , Schmitt Michael
Published: November 2024

Abstract. This manuscript investigates the effects of uncertainty methods applied to the problem of deep learning-based semantic segmentation of building footprints on moderate-resolution satellite imagery. While the recent efforts of big corporations to add information about building locations and sizes on a global or continental scale are generally valuable, still the overall challenge persists in identifying the spatial-temporal patterns of growing urbanization. In this work, we extend UNet-type architectures to perform binary building footprint classification based on Sentinel-2 imagery resulting in five different models. While previous studies focused on urban areas in the Western world we conduct all training and evaluation in India. All models are trained on Microsoft building footprint products while for evaluation purposes high-quality reference data is manually selected from regions with especially good open-street-map coverage. Quantitative and qualitative experiments are conducted where a significant performance gain is found for a model trained with a mixture of aleatoric and epistemic uncertainty measures. The performance gain is even more pronounced for subsequent quantitative multi-temporal change detection experiments.