Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated water losses assessment and water balance study over arid and semi-arid basins located in developing countries

Climate change, population growth, and water resources crossing political boundaries are the main issues threating water allocation for agricultural, industrial and domestic uses in developing countries. Integrated water resources management developed in a sustainable manner is essential to allow future generations to meet their water needs. A lack of data in developing countries is the biggest problem that can hinder developing necessary understandings. The Tigris river basin is a prime example, not only because it is located in a developing country, but also due to its long history of armed conflicts and breakdown of law and care. Unstable situation makes data collection difficult, available data poor in quality and the measuring tools and methods rudimental. The insufficient data lead to the impact of the climate change on water resources to be not conclusively detrained. This study shows the climate change impacts through investigate the evapotranspiration (ET) changes over the years. Five potential evapotranspiration models have been studied and classified according to the complexity in terms of the number of variables. Choosing the most suitable ET model helped to fill and reconstruct gaps in historical data sets. The statistical downscaling model SDSM was used to predict the evapotranspiration changes for the next 100 years. Google Earth and 3DRoutBuilder helped to produce an entire river profile with a simple, good quality representation of river networks. That aid the run of the hydrodynamic model (ISIS -1D) which has been utilised to produce water levels and water flow information to establish a robust river losses and water balance assessment for a river. Planning of alternative water resources schemes on river basins located in Arid-Semi Arid region needs an assessment of the hydrologic/hydraulic behaviours of that river. In view of this, the thesis further explores the sustainability of water quantities of rivers based on generated climate scenarios and population increases.
Is it possible that a gravity increase of 20 μGal yr−1 in southern Tibet comes from a wide‐range density increase?

With absolute gravimetric observations from 2010 to 2013 in the southern Tibet, Chen et al. (2016) reported a gravity increase of up to 20 μGal/yr and concluded that it is possible if there was a density increase in a disk range of 580 km in diameter. Here we used observations from the gravity satellites Gravity Recovery and Climate Experiment (GRACE) over 12 years to evaluate whether the model was practical, because a mass accumulation in such a large spatial range is well within the detectability ability of GRACE. The gravity trend based on their model is orders of magnitude larger than the GRACE observation, thus negating its conclusions. We then evaluated contributions from seasonal variation, lakes, glaciers, rivers, precipitation, and snowfall and concluded that these factors cannot cause such a large gravity signal. Finally, we discussed some possible explanations for the gravity increase of 40 μGal in two years.
Being and Time

Translators' Preface. Author's Preface to the Seventh German Edition. Introduction. Exposition of the Question of the Meaning of Being. 1. The Necessity, Structure, and Priority of the Question of Being. 2. The Twofold Task of Working out the Question of Being. Method and Design of our Investigation. Part I:. The Interpretation of Dasein in Terms of Temporality, and the Explication of Time as the Transcendental Horizon for the Question of Being. 3. Preparatory Fundamental Analysis of Dasein. Exposition of the Task of a Preparatory Analysis of Dasein. Being-in-the-World in General as the Basic State of Dasein. The Worldhood of the World. Being-in-the-World as Being-with and Being-One's-Self. The 'they'. Being-in as Such. Care as the Being of Dasein. 4. Dasein and Temporality. Dasein's Possibility of Being-a-Whole, and Being-Towards-Death. Dasein's Attestation of an Authentic Potentiality-for-Being, and Resoluteness. Dasein's Authentic Potentiality-for-Being-a-Whole, and Temporality as the Ontological Meaning of Care. Temporality and Everydayness. Temporality and Historicality. Temporality and Within-Time-Ness as the Source of the Ordinary Conception of Time. Author's Notes. Glossary of German Terms. Index.
Mass Spectrometric Characterization of Glycated p-Lactoglobulin Peptides Derived from Galacto-oligosaccharides Surviving the In Vitro Gastrointestinal Digestion

F. Javier Moreno,a Jesus Eduardo Quintanilla-L6pez,b,c Rosa Lebr6n-Aguilar,c Agustin Olano,a and Maria Luz Sanz a Instituto de Fermentaciones Industriales (CSIC), Madrid, Spain b Departamento de lngenieria Quirnica Industrial y del Medio Ambiente, ETS Ingenieros Industriales (V. P.M.) Madrid, Spain c lnstituto de Quimica-Fisica "Rocasolano" (CSIC), Madrid, Spain d lnstituto de Quimica Organica General (CSIC), Madrid, Spain
THE ROAD LESS TRAVELLED: A NEW PERSPECTIVE ON SUSTAINED COMPETITIVE ADVANTAGE THROUGH KNOWLEDGE CREATION

Knowledge and intellectual capital have become the primary bases upon which organisations construct their core competencies and are increasingly seen as the key to superior organisational performance (Lubit, 2001). At the same time, both the need to and difficulty associated with developing sustainable competitive advantages are rapidly increasing (ibid.). This paper argues that two roads lead to sustained competitive advantage in firms. The well-travelled road is largely based on conceptualising knowledge in terms of information and data and attempting to leverage organisational knowledge by focusing on the management and utilisation of information in organisations. The road less travelled is based on recognising the power of knowledge in general, and knowledge creation in particular, to stimulate creativity and innovation in organisations leading to sustained competitive advantage. On this road, it is recognised that truly innovative organisations “do not simply process information. they actually create new knowledge and information, from the inside out, in order to redefine both problems and solutions and, in the process, to re-create the environment” (Nonaka and Takeuchi, 1995, p.56). Despite the promise of the road less travelled, existing perspectives on knowledge creation are beset with a variety of epistemological and methodological problems. This paper develops a new perspective on knowledge creation by delving into existing conceptualisations and classifications of knowledge in literature, exploring the philosophical assumptions upon which they are based, probing the conceptual and methodological issues that surround these views and articulating a new perspective on knowledge creation to guide future research efforts.
System-scenario-based design of dynamic embedded systems

In the past decade, real-time embedded systems have become much more complex due to the introduction of a lot of new functionality in one application, and due to running multiple applications concurrently. This increases the dynamic nature of today's applications and systems, and tightens the requirements for their constraints in terms of deadlines and energy consumption. State-of-the-art design methodologies try to cope with these novel issues by identifying several most used cases and dealing with them separately, reducing the newly introduced complexity. This article presents a generic and systematic design-time/run-time methodology for handling the dynamic nature of modern embedded systems, which can be utilized by existing design methodologies to increase their efficiency. It is based on the concept of system scenarios, which group system behaviors that are similar from a multidimensional cost perspective—such as resource requirements, delay, and energy consumption—in such a way that the system can be configured to exploit this cost similarity. At design-time, these scenarios are individually optimized. Mechanisms for predicting the current scenario at run-time, and for switching between scenarios, are also derived. This design trajectory is augmented with a run-time calibration mechanism, which allows the system to learn on-the-fly during its execution, and to adapt itself to the current input stimuli, by extending the scenario set, changing the scenario definitions, and both the prediction and switching mechanisms. To show the generality of our methodology, we show how it has been applied on four very different real-life design problems. In all presented case studies, substantial energy reductions were obtained by exploiting scenarios.
Computing street centrelines from interstitial area - Application to the street network schematisation in the Metropolitan District of Quito (Ecuador)

This paper describes the foundations and the implementation of a GIS method that allows the generation of a network from an interstitial area. The method was specially designed to automatically create the urban road network using a georeferenced cadastre in a vector format. This tool is very useful for urban management authorities as it avoids the tedious manual digitising process or the frequently-used GPS acquisition survey (road tour) and as it also ensures an optimal overlay between the roads and blocks layers. The functionality was implemented in SavGIS, a GIS freeware developed by IRD (Institut de Recherche pour le Developpement, France) within the framework of the scientific cooperation with southern countries.
The Efficiency of the Health Belief Model in Predicting the Preventive Behaviors of Pregnant Women During the COVID-19 Pandemic in Iran

This study examined the efficiency of the health belief model in understanding preventive behaviors of pregnant women in Iran. A cross-sectional descriptive-analytical methodology study was conducted of pregnant women who were referred to a healthcare center in 2021. The data were the responses to a questionnaire designed for this study. The data were analyzed with SPSS 24 software, Amos 22 software, correlation tests, regression analysis, and independent t tests. In terms of the health belief model, pregnant women with high perceived susceptibility showed the highest vaccination rate, while those with high perceived barriers had the lowest. The model predicted 20% variance in the preventive behavior from COVID-19, with perceived susceptibility and cues to action being the strongest and weakest predictors of behavior, respectively. The conclusion of the study was that the health belief model was an appropriate model to guide the care of pregnant women.
Optimizing MRI Data Processing by exploiting GPU Acceleration for Efficient Image Analysis and Reconstruction

Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging modality, offering detailed anatomical insights without ionizing radiation The advent of Graphics Processing Units (GPUs) has stimulated a paradigm shift, propelling MRI techniques to new frontiers. Through parallel processing capabilities, GPUs have expedited real-time imaging, complex image reconstruction, noise reduction, and intricate data analysis. This paper provides an extensive survey on the GPUs based MRI techniques and its synergistic impact on core methodologies such as Diffusion Tensor Imaging (DTI) and Functional MRI (fMRI). Through parallel processing and frameworks like CUDA and OpenCL, GPUs have overcome computational hurdles in MRI data processing. Challenges like memory constraints and data transfer bottlenecks are addressed through hybrid CPU-GPU strategies and algorithmic enhancements. The integration of GPUs yields faster scans, enhanced image quality, and real-time insights, benefiting patient care and accelerating medical research. Considering the ethical issues regarding patient data privacy and algorithmic fairness, GPUs' potential in MRI research and development is evident. This paper concludes by looking ahead about the future of GPU based MRI, urging further exploration to uncover new possibilities and shape a transformative path for the future of medical imaging.
Applied Research on Artificial Strengthen Ecological Filter Bed Tecnology for the Improvement of the Water in TIAO ZI River Project

Artificial strengthen ecological filter bed is a new water purification tecnology which is in accordance with bio-chemicalbed and artificial wetlands principle. Through a small pilot plant,we investigated the influence these factors had on artificial ecological effect of filter bed from the temperature,aeration position,aeration intensity perspectives. The results showed that:the temperature of the filter bed runs a greater impact. However,at low temperatures,the filter bed has certain treatment effects. In different locations in the artificial filter bed enhanced aeration,filter bed in front is better than the back-end processing filter bed. Even at low temperatures,by strengthening the aeration,filter bed treatment efficiency can be increased by 10% or more. This indicates that the technology of artificial strengthen ecological filter bed is feasible and practical in river water quality improvement applications.
Texture Analysis of Retinal Layers in Spectral Domain OCT Images

Optical coherence tomography (OCT) is becoming one of the most important scanning techniques for the noninvasive diagnosis of retinal eye diseases. With the introduction of spectral domain OCT (SD-OCT), more complete macular scanning is achieved compared to what was possible using the previous time-domain OCT technique. In this paper, a method for automated segmentation of the spectral domain OCT images is reported together with a general approach for retinal abnormality diagnosis. Ten intraretinal layers are first automatically segmented into five surfaces and features are extracted in each surface locally to characterize texture properties across the macula. Abnormalities are then detected when the feature value of any subject is deviated from the preset normal range. This approach was applied to determine fluid-filled regions- SEADs (Symptomatic Exudate-Associated Derangements) in patients with macular edema, choroidal neovascularization (CNV) and central venous occlusion. This work aims to extract textural information from SD-OCT scans which can be used for clinically important applications.
Knowledge Level of Nurses about Hospital Triage: A systematic review study

Objective: Triage prioritizes patient care based on the severity of the injury and aims to facilitate the best possible recovery for the majority of individuals within the shortest possible timeframe. This concept holds paramount importance in the realm of emergency care. The objective of this study was to assess the knowledge level of triage nurses working in hospitals. Methods: This systematic review study encompassed an extensive analysis of relevant literature, drawing from international databases such as PubMed, Scopus, Web of Knowledge, and Google Scholar, as well as Iranian databases including SID, Magiran. The search spanned the period from 1980 to 2023. Results: In the present study, 22 articles were selected for data extraction. Across all these articles, it was consistently observed that the knowledge level of the nurses ranged from low to moderate. Furthermore, two interventional studies reported a notable increase in knowledge levels among nurses following training. Conclusion: Based on the findings from multiple studies, it is evident that the knowledge level of triage nurses generally falls within the low to moderate range. Consequently, it is advisable to conduct further studies in a similar vein and implement necessary actions to address this issue.
A simulation model for wilderness use in Yosemite National Park

A SIMULATION MODEL FOR WILDERNESS USE IN YOSEMITE NATIONAL PARK Kai Ross In the United States, legislation requires management of wilderness land to maintain its wild character. As the popularity of outdoor recreation grows, managing wilderness becomes not only more necessary, but also more difficult and complex. To help make important decisions, managers have been increasingly turning to computer simulation models for analysis and testing. This paper details the creation of a wilderness use simulation model for Yosemite National Park and demonstrates its utility. The model was implemented with ExtendSim OR simulation software and stochastically simulates trips taken by parties through Yosemite’s 53 wilderness zones throughout the season. We parameterized the model using the database of all backcountry permits issued in 2010. Temporal distribution of trip start dates was the only deterministic element of the model. Party size and starting trailhead were determined randomly for each party based on empirical distributions taken from the database. Instead of selecting trip itineraries from a fixed set, we used a matrix of state transition probabilities to randomly simulate each trip, both spatially and temporally. The model also included algorithms to simulate the tendency for parties to deviate spatially and temporally from their intended itinerary; these were parameterized with data collected from a random sample of park visitors. The model tracked backcountry use from trips the originated both from within and outside of the park. Validation statistics showed that the model successfully captured observed party and trip characteristics. We demonstrate the utility of the model by simulating two hypothetical scenarios in which visitation to high-use zones was reduced by lowering the number of parties allowed to start at popular trailheads and redistributing them to less popular trailheads. We also simulate a iii hypothetical steady-state scenario in which all trailheads are filled to quota on each day.
Gypsy Moth Management for Homeowners on Small Properties

Virginia Cooperative Extension programs and employment are open to all, regardless of race, color, national origin, sex, religion, age, disability, political beliefs, sexual orientation, or marital or family status. An equal opportunity/affirmative action employer. Issued in furtherance of Cooperative Extension work, Virginia Polytechnic Institute and State University, Virginia State University, and the U.S. Department of Agriculture cooperating. Mark A. McCann, Director, Virginia Cooperative Extension, Virginia Tech, Blacksburg; Alma C. Hobbs, Administrator, 1890 Extension Program, Virginia State, Petersburg. Gypsy Moth Management for Homeowners on Small Properties
Framework for Seismic Inversion of Full Waveform Data Using Sequential Filtering

Subsurface velocity inversion using full waveform modelling continues to be a challenging problem. Instead of approaching it as a deterministic optimization problem, it is here formulated in a probabilistic framework as a filtering problem, using shot data sequentially to update the estimation procedure. Such an approach has the potential of being more robust to e.g. noise and starting guess, but comes at the cost of more forward model evaluations. We present a small-sized synthetic example of using a sequential filtering method known as the Ensemble Kalman Filter for seismic velocity inversion and conclude with an outline of direction for further investigations.
Comparison of Machine Learning Methods for Estimating Leaf Area Index and Aboveground Biomass of Cinnamomum camphora Based on UAV Multispectral Remote Sensing Data

UAV multispectral technology is used to obtain leaf area index (LAI) and aboveground biomass (AGB) information on Cinnamomum camphora (C. camphora) and to diagnose the growth condition of Cinnamomum camphora dwarf forests in a timely and rapid manner, which helps improve the precision management of Cinnamomum camphora dwarf forests. Multispectral remote sensing images provide large-area plant spectral information, which can provide a detailed quantitative assessment of LAI, AGB and other plant physicochemical parameters. They are very effective tools for assessing and analyzing plant health. In this study, the Cinnamomum camphora dwarf forest in the red soil area of south China is taken as the research object. Remote sensing images of Cinnamomum camphora dwarf forest canopy are obtained by the multispectral camera of an unmanned aerial vehicle (UAV). Extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), random forest (RF), radial basis function neural network (RBFNN) and support vector regression (SVR) algorithms are used to study the correlation and estimation accuracy between the original band reflectance, spectral indices and LAI and AGB of Cinnamomum camphora. The results of this study showed the following: (1) The accuracy of model estimation based on RF is significantly different for different model inputs, while the other four models have small differences. (2) The accuracy of the XGBoost-based LAI model was the highest; with original band reflectance as the model input, the R2 of the model test set was 0.862, and the RMSE was 0.390. (3) The accuracy of the XGBoost-based AGB model was the highest; with spectral indices as the model input, the R2 of the model test set was 0.929, and the RMSE was 587.746 kg·hm−2. (4) The XGBoost model was the best model for the LAI and AGB estimation of Cinnamomum camphora, which was followed by GBDT, RF, RFNN, and SVR. This research result can provide a theoretical basis for monitoring a Cinnamomum camphora dwarf forest based on UAV multispectral technology and a reference for rapidly estimating Cinnamomum camphora growth parameters.
Analysis of household demand for improved sanitation: the case of green latrines in Dapaong city in Northern Togo

ABSTRACT This article analyses the socioeconomic factors that influence the desire to adopt a green latrine. Using two econometric models applied to microdata from Dapaong, we highlight a number of new results. In particular, we show that the green latrine is popular among rural households seeking to increase the productivity of their land and their agricultural output. Social networks also play an important role. The privacy offered by the closed latrine influences the desire for its adoption. Finally, the close links among access to safe water, access to improved sanitation and the prevalence of waterborne diseases are highlighted.
Rapid Quantification of Bone Collagen Content by ATR-FTIR Spectroscopy

Abstract Expensive and time-consuming preparation procedures for radiocarbon and stable isotope analyses can be conducted on archaeological bone samples even if no collagen is preserved. Such unsuccessful preparation can lead to the partial destruction of valuable archaeological material. Establishing a rapid prescreening method for evaluating the amount of bone collagen while minimizing the impact of sampling constitutes a challenge for the preservation of archaeological collections. This study proposes and discusses a new methodology to detect and quantify collagen content in archaeological bone samples by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. A total of 42 Pleistocene to modern bone samples were selected according to their nitrogen content measured using an elemental analyzer. Comparison of collagen content estimation using ATR-FTIR and mass spectrometry reveals that some of the studied samples are contaminated by a nitrogen source coming from the burial environment. Two different FTIR calibration approaches were tested on the uncontaminated samples: peak-to-peak ratio and multivariate regression (PLS). The two approaches yield similar results with a good correlation of ATR-FTIR analyses and N wt% from 0.7 to 4wt% (R²=0.97–0.99; standard error of estimation ±0.22 to 0.25wt%). While collagen content remains difficult to detect in poorly preserved bones (less than ~3wt%), ATR-FTIR analysis can be a fast alternative for sample screening to optimize the sampling strategy and avoid partial destruction of valuable samples that do not contain enough collagen for further analysis.