Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Multisatellite Altimeter Data Assimilation on Wave Analysis and Forecast

Satellite altimetry has become an important discipline in the development of sea-state forecasting or more generally in operational oceanography. Météo-France Marine and Oceanography Division is much involved in altimetry, in which it is also one of the main operational customers. Sea-state forecasts are produced every day with the help of numerical models assimilating Fast Delivery Product altimeter data from ESA ERS-2 satellite, available in real-time (3–5 h). These forecasts are transmitted to seamen as part of safety mission of persons and properties, or specific assistance for particular operations. With the launch of ENVISAT (from ESA, launched on 1 March 2002, to take over the ERS mission) and JASON-1 (from CNES/NASA, launched on 7 December 2001, successor of TOPEX/Poseidon), we have an unprecedented opportunity of improved coverage with the availability in quasi-real-time of data from several altimeters. The objective of this study is to evaluate the impact of using multisources of altimeter data in real-time, to improve wave model analyses and forecasts, at global scale. Since July 2003, Météo-France injects the wind/wave JASON-1 Operational Sensor Data Record on the WMO Global Transmitting System, making them available in near real-time to the international meteorological community. Similarly, fast delivery altimeter data of ENVISAT will improve coverage and contribute to the constant progress of marine meteorology. For this purpose, significant wave height time series were generated using the Wave Model WAM and the assimilation of altimeter wave heights from two satellites ERS-2 and JASON-1. The results were then compared to Geosat Follow-On (GFO, U.S. Navy Satellite) and moored buoy wave data. It is shown that the impact of data assimilation, when two (ERS-2 and JASON-1) or three (ERS-2 with JASON-1 and GFO) sources of data are used instead of one (ERS-2), in term of significant wave height, is larger in wave model analyses but smaller in wave model forecasts. However, there is no improvement in terms of wave periods, both in the analysis and forecast periods.
Modelling the on-road traffic emissions from Catalonia (Spain) for photochemical air pollution research : weekday/weekend differences

On-road traffic can vary between weekdays and weekends, both in timing and magnitude. It influences atmospheric emissions behaviour and tropospheric ozone concentrations. Giving priority to the bottom-up approach, we developed the high spatial (1 km 2 ) and temporal (1 hour) resolution EMICAT2000 model, to estimate atmospheric emissions for the year 2000 from Catalonia (Spain). Its on-road traffic component was built with a high-resolution digital road-map and considered the fleet of vehicles, traffic intensity and profiles for weekdays and weekends; for every highway and the most important roads and urban streets (daily average traffic > 5000). Most of the emission factors were taken from the top-down COPERTIII model. We used specific Carbon Bond 4 profiles for NMVOC speciation. During the year 2000, emissions of NO x were 62.4 kt yr -1 , VOCs 50.5 kt yr -1 , CO 259.0 kt yr -1 , SO 2 1.3 kt yr -1 , PST 15.7 kt yr -1 . Emissions are mainly located on the Metropolitan Area of Barcelona and on the axis of highways following the coastline. Gasoline vehicles represent 70% of the park, but they emit ∼57% of NO x and ∼92% of VOC. Due to the drop of heavy duty vehicle traffic, NO x emissions on weekends are ∼22% lower and emission of VOC are only slightly higher. Influences of these changes on ozone pollution could be deeply understood using a chemical transport model. Development of high spatial resolution emission is expensive both in time and resources, but the benefits and potential uses completely iustifv the investment.
The Use of the DEMATEL Method to Analyze Factors Influencing the Usage of Inland Waterway Transport

: The article presents the results of the DEMATEL method (Decision Making Trial and Evaluation Laboratory) method. The method had been used to identify and analyze cause and effect relationships between factors that influence the use of inl and waterway transport. The author took into account interactions between seven factors. The factors were: (1) accessibility and condition of ports, transshipment infrastructure, shipyards, and service areas, (2) operating parameters of the linear infrastr ucture (waterways and locks), (3) technical condition of the fleet, (4) human errors committed by crew, infrastructure operators, and/or decision makers, (5) climate and weather conditions (floods, droughts, wind, ice, groundwater level), (6) financing the water transport system - repairs, ongoing maintenance of infrastructure; subsidies and discounts for shipowners, (7) availability of information (e.g. navigation messages), method of data collection and distribution, communication and promotion of inland navigation. The analysis based on the DEMATEL method used the survey conducted among Polish experts in the field of inland waterway transport. The results of the analysis show that the most influencing factor is financing, and the most important cause infl uencing inland waterway transport is climate.
Power consumption control of multi-pump systems of the main water drainage in underground mines based on the Mamdani fuzzy inference system

The article considers synthesis of an expert system for controlling electric power consumption by pumps of main water drainage facilities of an underground mine on the basis of the Mamdani fuzzy inference algorithm. The proposed system has a MISO-structure (multiple-input, single-output) with two input variables, such as water inflow and power cost as well as one output coordinate – power of pumping units. Two bases of fuzzy rules such as conjunction (AND) and disjunction (OR) are formed. By simulation modelling, a comparative analysis of fuzzy control systems for power consumption by water drainage facilities is carried out, as well as a system without control, when the pump performance is stabilized, during week and month periods. It is established that OR-rule based systems can reduce power costs by 1.89% during a week and by 2.28% during a month, and AND-rule based systems by 4.13%, as well as by 5.43% during week and month, respectively. At the same time, we note that the economic effect is achieved not through a decrease in power consumption, but by adjusting the operation mode of the water drainage facility, which involves ensuring maximum efficiency of groundwater drainage when the power cost is high, and minimum efficiency when it is low.
A Novel Graph Neural Network for Zone-Level Urban-Scale Building Energy Use Estimation

Buildings are highly responsible for total energy consumption in cities; therefore, accurate estimation of building energy consumption is essential for developing energy-efficient strategies on an urban scale. Data-driven urban building energy models can predict energy use with high precision and low computational cost. In recent years, machine learning, especially neural networks, emerged as the prominent method for predicting energy load for buildings. These models typically use different input features on building form, occupancy and operation. However, they remain inadequate in capturing the complex inter-dependencies (i.e., heat transfer) between units in multi-zone buildings, as they do not have an explicit representation of the neighborhood relations between zones. The precision of data-driven models can be improved using Graph neural networks (GNN) that can capture the underlying relationships and dependencies between different building elements. In this paper we propose a novel GNN model that surpasses the current state-of-art methods for the prediction of zone-level heating energy use. We applied the methodology in a residential neighborhood consisting of 5866 buildings and 64462 zones. We use zone-level features regarding their geometry, material thermal characteristics, internal loads as node features, inter-zone parameters as edge features (total area and U value of the adjacent surfaces) and weather parameters. The results showed that our proposed model provides improvements over alternative approaches for precise prediction of urban building energy consumption.
Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets

Dental diagnostic imaging has progressed towards the use of advanced technologies such as 3D image processing. Since multidetector computed tomography (CT) is widely available in equine clinics, CT-based anatomical 3D models, segmentations, and measurements have become clinically applicable. This study aimed to use a 3D segmentation of CT images and volumetric measurements to investigate differences in the surface area and volume of equine incisors. The 3D Slicer was used to segment single incisors of 50 horses’ heads and to extract volumetric features. Axial vertical symmetry, but not horizontal, of the incisors was evidenced. The surface area and volume differed significantly between temporary and permanent incisors, allowing for easy eruption-related clustering of the CT-based 3D images with an accuracy of >0.75. The volumetric features differed partially between center, intermediate, and corner incisors, allowing for moderate location-related clustering with an accuracy of >0.69. The volumetric features of mandibular incisors’ equine odontoclastic tooth resorption and hypercementosis (EOTRH) degrees were more than those for maxillary incisors; thus, the accuracy of EOTRH degree-related clustering was >0.72 for the mandibula and >0.33 for the maxilla. The CT-based 3D images of equine incisors can be successfully segmented using the routinely achieved multidetector CT data sets and the proposed data-processing approaches.
Which Policies and Factors Drive Electric Vehicle Use in Nepal?

Electric vehicles (EVs) offer a viable technological solution for mitigating greenhouse gas emissions in the transportation industry, addressing pressing societal concerns regarding climate change, air pollution, and sustainable energy consumption. To effectively promote widespread adoption of EVs, it is crucial to understand consumer preferences and evaluate market dynamics. In Nepal, where proven fossil fuel reserves are absent, the government is actively working towards accelerating EV adoption, leveraging the nation’s significant hydroelectric power generation potential to fulfill EVs’ charging demands. To gain insight into consumer preferences and evaluate market dynamics regarding EVs in Nepal, this study employs a comprehensive approach. Stated preference data are collected through a meticulously designed survey, and sophisticated analytical techniques, namely, the mixed logit model and latent class model, are applied for estimation purposes. The results of this study show that potential EV consumers with small family sizes, lower monthly travel distances, heightened environmental awareness, and substantial knowledge about electric vehicles are more inclined to embrace EV technology. Notably, the study highlights that a reduction in the purchase price exerts the most significant influence on increasing consumers’ likelihood of adopting battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Market simulation results suggest that a policy mix scenario, encompassing a combination of supportive measures, proves more effective in promoting EV adoption compared to relying on single policy initiatives. Furthermore, through latent class estimation, the study identifies three distinct classes of consumers within Nepal, each exhibiting significant variations in preferences. Recognizing and addressing these variations within policy frameworks is crucial for the successful promotion and widespread acceptance of EVs in Nepal.
Assessing water source and channel type as factors affecting benthic macroinvertebrate and periphyton assemblages in the highly urbanized Santa Ana River Basin, California

The Santa Ana River basin is the largest stream system in Southern California and includes a densely populated coastal area. Extensive urbanization has altered the geomorphology and hydrology of the streams, adversely affecting aquatic communities. We studied macroinvertebrate and periphyton assemblages in relation to two categorical features of the highly engineered hydrologic system—water source and channel type. Four water sources were identified—natural, urban-impacted groundwater, urban runoff, and treated wastewater. Three channel types were identified—natural, channelized with natural bottom, and concrete-lined. Nineteen sites, covering the range of these two categorical fea- tures, were sampled in summer 2000. To minimize the effects of different substrate types among sites, artificial substrates were used for assessing macroinvertebrate and periphyton assemblages. Physical and chemical variables and metrics calculated from macroinvertebrate and periphyton assemblage data were compared among water sources and channel types using analysis of variance and multiple com- parison tests. Macroinvertebrate metrics exhibiting significant (P < 0.05) differences between water sources included taxa and Ephemeroptera-Plecoptera-Trichoptera richness, relative richness and abun- dance of nonchironomid dipterans, orthoclads, oligochaetes, and some functional-feeding groups such as parasites and shredders. Periphyton metrics showing significant differences between water sources included blue-green algae biovolume and relative abundance of nitrogen heterotrophic, eutrophic, motile, and pollution-sensitive diatoms. The relative abundance of trichopterans, tanytarsini chirono- mids, noninsects, and filter feeders, as well as the relative richness and abundance of diatoms, were significantly different between channel types. Most physical variables were related to channel type, whereas chemical variables and some physical variables (e.g., discharge, velocity, and channel width) were related to water source. These associations were reflected in correlations between metrics, chemical variables, and physical variables. Significant improvements in the aquatic ecosystem of the Santa Ana River basin are possible with management actions such as conversion of concrete-lined channels to channelized streams with natural bottoms that can still maintain flood control to protect life and property.
Temporal assessment of GCM-driven hydroclimatic conditions for the Alberta oil sands region, Canada

Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns which characterize these cold regions. In this study, we used datasets from 12 climate simulations associated to seven global climate models and four future scenarios and participating in the Coupled Model Intercomparison Project Phase 6, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series, to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework with the Alberta oil sands region in Canada, to support the monitoring of environmental changes in this region, relative to the established baseline 1985-2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the Standardized Precipitation Evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study, suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.
Studies on the application of stand spatial structure parameters based on the relationship of neighborhood trees

Forest management is a perpetual theme of forestry development, whose principle is following the law of nature, which, in other words, is abiding by the regular pattern of nature to achieve structural adjustment of forest given a certain objective. Forest structure helps us understand the history of forest development, its current condition and the future development of ecosystem. This is crucial to analyzing and managing forest ecosystem. The structure of forest characterized by the diversity of spatial position, species and dimension, to a greater degree, depends on the spatial relationship of neighborhood trees. Stand spatial structure parameters based on the relationship of neighborhood trees like uniform angle index, mingling and dominance are widely used both internationally and domestically in the studies of stand spatial structure analysis, competition among trees, calculation of dominance, species diversity measurement and structure recovery and reconstruction as well as optimization adjustment. This has yielded positive results. How to integrate the three aspects of spatial structure into one entity for the purpose of analyzing the features and changes of stand spatial structure points to an important direction of future research. Future research will focus on the 3-dimensional or bivariate distribution of variables, studies on which has become another key aspect of current and future forest research due to its visualization.
Methodology for determining the availability of natural moistening of the territory by hydrometeorological conditions for the needs of land reclamation

The paper considers one of the important problems of water use – determination of the humidification conditions of the territory for the hydromelioration needs. The methodical approach to determining the probability of exceeding natural moisture by a group of criteria is highlighted. Nowadays there is no single, reliable and simple method for determining this indicator. Assessing climatic conditions, considering their impact on crop productivity, it is necessary to take into account hydrometeorological factors that have a decisive influence on the development of crops and, accordingly, determine their yield. These include, first of all, precipitation, moisture reserves in the soil, evapotranspiration and other complex indicators that allow to take into account the distribution of meteorological factors during the growing season of the crop and their possible negative impact on the development of the plant, because each crop requires a certain optimal regime of soil temperature and moisture at different stages of its development. The article presents the solution of the set tasks on the example of winter wheat crops according to the data of the Dnipro meteorological station (airport).
Treatment of Imbalance Dataset for Human Emotion Classification

Developments in biomedical science, signal processing technologies have led Electroencephalography (EEG) signals to be widely used in the diagnosis of brain disease and in the field of Brain-Computer Interface (BCI). The collected EEG signals are processed using Machine Learning-Random Forest and Naive Bayes-and Deep Learning-Recurrent Neural Network (RNN), Neural Network (NN) and Long Short Term Memory (LSTM)-Algorithms to obtain the recent mood of a person. The Algorithms mentioned above have been imposed on the data set in order to find out what the person is feeling at a particular moment. The following thesis is conducted to find out one of the following moods (happy, surprised, disgust, fear, anger and sadness) of a person at an instant, with an aim to obtain the result with least amount of time delay as the mood differs. It is pretty obvious that the accuracy of the output varies depending upon the algorithm used, time taken to process the data, so that it is easy for us to compare the reliability and dependency of a particular algorithm to another, prior to its practical implementation. The imbalance data sets that were used had an imbalanced class and thus, over fitting occurred. This problem was handled by generating Artificial Data sets with the use of SMOTE Oversampling Technique.
Estimation of Pollen Production in Fruit Crops

The use of the haemocytometer for the estimation of pollen production in papaya, pomegranate and sapota is dealt with in this papor. The marked variation in the pollen output among the varietes has been brought out. The number of pollon grains per anther varied from 682 to 3,297 in the sapotn, 8,050 to 12,465 in the papaya and from 15,982 to 23,170 in the pomegranate.
Terrestrial analogs for interpretation of infrared spectra from the Martian surface and subsurface: Sulfate, nitrate, carbonate, and phyllosilicate‐bearing Atacama Desert soils

[1] Hyperarid ( 6.5 μm) was not suited for salt and phyllosilicate detection because of overlapping spectra from primary silicates (e.g., feldspar), water and carbon dioxide. The visible near-infrared (0.35–2.5 μm) region was suited for detecting carbonate, nitrate, gypsum water of hydration, and phyllosilicate hydroxyls without interference from primary silicates. However, gypsum water of hydration features can obscure phyllosilicate hydroxyl, carbonate and nitrate, features if gypsum levels are high. Overtone/combination absorption features in the midinfrared were determined to be the best indicators of sulfate (4.48–4.70 μm), nitrate (4.12 μm), and carbonate (3.98 μm) because interferences from overlapping primary silicate and water features are not present in this region. Interferences from CO2 and thermal emission effects in the overtone/combination region are possible but may be minimized by corrective techniques. Infrared analysis of Atacama Desert soils can provide insight into the spectral search of sulfate, nitrate, carbonate, and phyllosilicate containing soils on Mars.
Assessment of effects in advances of accounting technologies on quality financial reports in Jordanian public sector

The study aimed to examine the effects of accounting technology improvements on the generation of accurate and reliable financial reports in the public sector of Jordan. In order to carry out this inquiry, the researchers set research goals and formulated null hypotheses that were derived from these objectives and afterwards used in the study. The study used an ex-post facto survey methodology as its research technique. The study sample included 250 persons employed at the Ministry of Finance in Jordan. The research included a sample size including 152 people. A questionnaire was used as the primary tool for data collection in this study. The validity of the instrument was established by an evaluation conducted by experts specialising in the field of testing and measurement. The evaluation of the instrument's dependability was performed using the Cronbach Alpha reliability approach, yielding a reliability coefficient ranging from 0.73 to 0.85. The findings of this research demonstrate that the instrument has a significant level of dependability. The data obtained from the surveys underwent analysis using the Pearson Product-Moment Correlation (PPMC) and regression analysis approaches. The present study offers empirical data and affirms the growing significance of financial reporting in the global economic landscape. Ensuring unwavering trust in the financial information pertaining to the public sector has considerable significance for investors. The article proposes that the establishment of a comprehensive framework of guidelines for enterprises' information technology infrastructure would be advantageous for regulatory bodies, such as the Jordan Central Bank. The aim of this method is to reduce the potential danger of the public sector being overwhelmed by outdated technology.