Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Water Quality Regulation Functions in Southwestern Europe Watersheds

Estimation at large scale of the water quality regulation services is still lacking. It is essential to develop methodological approaches to quantify nutrient-related functions’ distribution. The present study aims to quantify nitrate-related ecological functions through nitrate net balance (NNB), nitrate removal (NR), and nitrate production (NP). This study explores the spatiotemporal dynamics of these indicators in South-Western Europe (SUDOE, 216 subsystems over 81 basins) at a monthly scale from 2000 to 2010. We use the Soil and Water Assessment Tool model to simulate nutrient transfer at the subsystem scale (~3000 km2) and calculate ecological functions. The modeled NNB is validated at the subsystem scale by comparing with NNB predicted at the water body scale (~60 km2) over the Garonne watershed (France). Hot spots of NR are located in the south of SUDOE, characterized by a warmer and dryer climate, whereas NP hot spots are located in the most anthropized streams. The mean NNB (the balance between NP and NR) at the subsystem scale for the SUDOE territory reaches −3.5 and −2.8 gN m−2 day−1 during the most active seasons. The results highlight drivers influencing NR such as streamflow, river slope, and hydrological alteration. Getting an overview of where and when these nitrate regulation functions (NP and NR) occur is essential for socio-ecosystem sustainability and this study highlights its sensitivity to anthropogenic stressors.
Evaluation of Adsorptive and Biological Mode Dbp Removal in Activated Carbon Filters

Yang, Nathan T. (M.S., Environmental Engineering) Evaluation of Adsorptive and Biological DBP Removal in Activated Carbon Filters Thesis directed by R. Scott Summers, Professor, Department of Civil, Environmental, and Architectural Engineering, University of Colorado at Boulder Small drinking water systems face unique compliance challenges with regards to many water quality parameters, including disinfection-by-product (DBP) levels in the distribution system. Filtration with granular activated carbon (GAC) can be an effective technology for the removal of total organic carbon (TOC) and DBPs. The objectives of this thesis were to develop and evaluate the use of GAC in the distribution system to meet DBP regulations under both adsorptive and biological modes. It was hypothesized that a post-treatment reactor strategically located in the distribution system will offer small systems a cost-effective alternative to controlling total trihalomethanes (TTHMs), sum of five haloacetic acids (HAA5s) and other unregulated DBPs. A total of six adsorptive rapid small scale column tests (RSSCTs) and three pilot scale biofilters were operated to investigate the effects of GAC type, source water quality, temperature and empty bed contact time (EBCT) on the adsorption and biodegradation of TOC and DBPs in treated drinking water. Experimental results show that adsorption with bituminous GAC is an effective treatment strategy for the removal of TOC and TTHMs through at least 6,000 bed volumes (42 days at 10min EBCT) and often longer depending on influent conditions. Pore surface diffusion model (PSDM) analysis indicated that the presence of both natural organic matter (NOM) and co-solutes are important to consider when analyzing THM breakthrough, with THM adsorbability being the most important factor in determining
Increasing the reactive sites of ZnO nanoparticles by Li doping for ethanol sensing

Gas sensing in metal oxide semiconductors can be enhanced by creating more number of gas adsorption (reactive) sites. With this aim, Li-doped ZnO nanoparticles with chunk-shaped morphology is synthesized, which provide more adsorption sites due to its morphology and due to the presence of oxygen vacancies. XRD analysis of undoped and Li-doped (2 and 5 at.%) ZnO nanoparticles revealed the hexagonal wurtzite structure and SEM and TEM analyses showed the chunk-shaped morphology in all the samples. The presence of pores and voids between the nanoparticles in the chunk-shaped morphology plays a significant role in the gas sensing mechanism. PL analysis showed that compared to ZnO nanoparticles, Li-doped ZnO nanoparticles possess more number of oxygen vacancies, zinc interstitials, and Li interstitials, the important factors that enhance the sensitivity of Li-doped ZnO nanoparticles. As expected, Li-doped ZnO nanoparticles showed more sensitivity compared to undoped ZnO nanoparticles toward ethanol.
Tenses

Liberal Arts 200 ▪ writingcenter.boisestate. edu ▪ 208-426-1298 Make us central to your writing! Tenses In English, there are three basic verb tenses: past, present, and future. Within each of these tenses, there are different types of tenses: perfect, present, simple, and progressive. Additionally, there is the conditional, which expresses actions that are theoretical. Verb Tense Examples in the affirmative (A), in the negative (N), and in question (Q) Usage Words that signal when to use this tense
Stereolithography

The stereolithography (SLA) process and its methods are introduced in this chapter. After establishing SLA as pertaining to the high-resolution but also high-cost spectrum of the 3D printing technologies, different classifications of SLA processes are presented. Laserbased SLA and digital light processing (DLP), as well as their specialized techniques such as two-photon polymerization (TPP) or continuous liquid interface production (CLIP) are discussed and analyzed for their advantages and shortcomings. Prerequisites of SLA resins and the most common resin compositions are discussed. Furthermore, printable materials and their applications are briefly reviewed, and insight into commercially available SLA systems is given. Finally, an outlook highlighting challenges within the SLA process and propositions to resolve these are offered.
Effect of pH of soft drinks on force decay in orthodontic power chains

The aim of this study was to evaluate the effects of Teh Botol, Buavita, Coca-Cola, and distilled water on force decay in orthodontic power chains. Twenty-eight power chains (Ormo; closed type; transparent) were fixed onto acrylic frameworks (inter-hook distance, 40 mm) and immersed in sweet tea, juice, softdrink, and distilled water. Force decay was measured at 1, 24, 42, 72, 168, and 336 h after immersion using a tension gauge. The force levels were decreased after the immersion of power chains in all four solutions. However, acidity did not significantly influence the force decay of the power chains.
In-situ 3D CT-XAFS Imaging of Pt/C Cathode Catalysts in Polymer Electrolyte Fuel Cell during Degradation Processes by Anode Gas Exchange Cycles

Polymer electrolyte fuel cell (PEFC) is one of the most efficient clean energy generation systems with high power density at low temperatures, which has a great advantage for its automotive applications. However, a major problem is practically insufficient durability of the membrane electrode assembly (MEA) in PEFC under the operating conditions, especially during startup/shutdown cycles of PEFC, where the feed gases are exchanged. When the anode feed gas exchange cycles are repeated, while keeping the cathode under air, the cathode catalyst degradation proceeds [1, 2]. To visualize 3D degradation of the cathode catalyst layer, we measured the structural and electronic changes of Pt nanoparticles in the PEFC MEA during the anode-gas exchange (AGEX) cycles by in-situ CT-XAFS imaging technique [3, 4] at our beamline BL36XU in SPring-8.
An RNA-Seq-Based Framework for Characterizing Canine Prostate Cancer and Prioritizing Clinically Relevant Biomarker Candidate Genes

Prostate cancer (PCa) in dogs is a highly malignant disease akin to its human counterpart. In contrast to the situation in humans, multi-gene approaches facilitating risk stratification of canine PCa are barely established. The aims of this study were the characterization of the transcriptional landscape of canine PCa and the identification of diagnostic, prognostic and/or therapeutic biomarkers through a multi-step screening approach. RNA-Sequencing of ten malignant tissues and fine-needle aspirations (FNA), and 14 nonmalignant tissues and FNAs was performed to find differentially expressed genes (DEGs) and deregulated pathways. The 4098 observed DEGs were involved in 49 pathways. These 49 pathways could be grouped into five superpathways summarizing the hallmarks of canine PCa: (i) inflammatory response and cytokines; (ii) regulation of the immune system and cell death; (iii) cell surface and PI3K signaling; (iv) cell cycle; and (v) phagosome and autophagy. Among the highly deregulated, moderately to strongly expressed DEGs that were members of one or more superpathways, 169 DEGs were listed in relevant databases and/or the literature and included members of the PCa pathway, oncogenes, prostate-specific genes, and druggable genes. These genes are novel and promising candidate diagnostic, prognostic and/or therapeutic canine PCa biomarkers.
Accurately Solving Rod Dynamics with Graph Learning

Iterative solvers are widely used to accurately simulate physical systems. These solvers require initial guesses to generate a sequence of improving approximate solutions. In this contribution, we introduce a novel method to accelerate iterative solvers for rod dynamics with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations. Unlike existing methods that aim to learn physical systems in an end-to-end manner, our approach guarantees long-term stability and therefore leads to more accurate solutions. Furthermore, our method improves the run time performance of traditional iterative solvers for rod dynamics. To explore our method we make use of position-based dynamics (PBD) as a common solver for physical systems and evaluate it by simulating the dynamics of elastic rods. Our approach is able to generalize across different initial conditions, discretizations, and realistic material properties. We demonstrate that it also performs well when taking discontinuous effects into account such as collisions between individual rods. Finally, to illustrate the scalability of our approach, we simulate complex 3D tree models composed of over a thousand individual branch segments swaying in wind ﬁelds.
Supplementary Material: Intention-based Long-Term Human Motion Anticipation

The encoder el and decoder dl in Figure 3 of the paper are both represented as single-layer GRUs with 16 hidden units. The decoder has an additional softmax layer to generate class probabilities. Dlabel is a feed-forward neural network with a single hidden layer with 32 units, a ReLU non-linearity, and a single unit sigmoid output. The input to Dlabel are the predicted output labels of dl, stacked for 25 frames. The pose encoder ep and decoder dp are represented as GRUs where ep is a single-layer GRU with 512 hidden units and where dp is a three-layer GRU with 512 hidden units and dropout rate 0.3. The encoder output hp is passed as hidden state to the first decoder layer, while the remaining two layers are initialized with a zero hidden state. The noise state z is concatenated to all three hidden states before passing to the decoder. The pose discriminator Dpose is represented as one hidden layer feed-forward neural network with 512 hidden units, ReLU non-linearity and a single unit sigmoid output. The input to Dpose are the predicted output labels of dp stacked for 25 frames. Each pose has dimension 54. The implementation of the NDMS metric and the source code for the approach are available at https: //github.com/jutanke/human_motion_ndms.
Barriers and facilitators to lung cancer screening in the United States: A systematic review of the qualitative literature

Introduction: Lung cancer is the second most common cancer and the leading cause of cancer-associated mortality in men and women in the United States. We sought to identify factors associated with Lung Cancer Screening (LCS) participation. Methods: To identify barriers and facilitators to lung cancer screening, we systematically reviewed the literature for qualitative research studies evaluating lung cancer screening conducted in the United States. Articles were analyzed using a grounded approach (open coding, axial coding, and selective coding). Findings are discussed using the Socio-ecological Model (SEM), a theoretical model of health behaviors. Themes were identified and organized by their relevance to the SEM: individual patient, individual provider, interpersonal, cultural, and organizational levels. Results: Themes facilitating participation include: prioritizing patient education, quality of communication, quality of provider-initiated encounter/coordination of care (individual patient and provider levels), quality of the patient-provider relationship (interpersonal level); perception of a life’s value and purpose (cultural level); and, quality of tools and care coordination (organizational level). Themes coded as barriers include: knowledge/capacity, behavior/attitude, comorbidities, and perception (individual patient level); education, pitfalls, process, and policies (provider level); patient-provider relationship, patient education, and quality communication (interpersonal level); distrust in the system, fatalistic beliefs, perception of aging (cultural level); and, access to resources, care coordination, and implementing lung cancer screening (LCS; organizational level). Conclusion: Few qualitative research studies are available evaluating barriers and facilitators to LCS participation in the US. Major facilitators of lung cancer screening include patient education, high-quality communication, perception of life’s value, and decision-making tools. Major barriers to lung cancer screening include knowledge, patient-provider rapport, distrust in the system, and access to resources. More qualitative studies are required to determine if these identified barriers and facilitators are transferrable to all LCS eligible population. Journal of Health and Social Sciences 2021; 6,3:333-348 The Italian Journal for Interdisciplinary Health and Social Development
Синтез и преобразования программ Program Synthesis and Transformations

A polyprogram is a generalization of a program which admits multiple deﬁnitions of a single function. Such objects arise in diﬀerent transformation systems, such as the Burstall–Darlington framework or equality saturation. In this paper, we introduce the notion of a polyprogram in a non-strict ﬁrst-order functional language. We deﬁne denotational semantics for polyprograms and describe some possible transformations of polyprograms, namely we present several main transformations in two diﬀerent styles: in the style of the Burstall–Darlington framework and in the style of equality saturation. Transformations in the style of equality saturation are performed on polyprograms in decomposed form, where the diﬀerence between functions and expressions is blurred, and so is the diﬀerence between substitution and unfolding. Decomposed polyprograms are well suited for implementation and reasoning, although they are not very human-readable. Wealsointroduce the notion of polyprogram bisimulation which enables a powerful transformation called merging by bisimulation , corresponding to proving equivalence of functions by induction or coin-duction. Polyprogram bisimulation is a concept inspired by bisimulation of labelled transition systems, but yet it is quite diﬀerent, because polyprogram bisimulation treats every deﬁnition as self-suﬃcient, that is a function is considered to be deﬁned by any of its deﬁnitions, whereas in an LTS the behaviour of a state is deﬁned by all transitions from this state. We present an algorithm for enumerating polyprogram bisimulations of a certain form. The algo-rithm consists of two phases: enumerating prebisimulations and converting them to proper bisimulations. This separation is required because polyprogram bisimulations take into account the possibility of parameter permutation. We prove correctness of this algorithm and formulate a certain weak form of its completeness. Thearticle is published in the
Literature Review of Robot Force Control Method

This paper introduces the robot force control methonds, ‘through the arm’ and ‘around the arm’. In the manufacturing industry, there are many operations that require a specific amount of force to be applied. A literature review of robot force control systems is presented. This paper aims to compare two methods ‘through the arm’ and ‘around the arm’.
DeepMistake: Which Senses are Hard to Distinguish for a Word­in­Context Model

In this paper, we describe our solution of the Lexical Semantic Change Detection (LSCD) problem. It is based on a Word­in­Context (WiC) model detecting whether two occurrences of a particular word carry the same meaning. We propose and compare several WiC architectures and training schemes, and also different ways to convert WiC predictions into final word scores estimating the degree of semantic change. We participated in the RuShiftEval LSCD competition for the Russian language, where our model achieved 2nd best result during the competition. During post­evaluation experiments we improved the WiC model and man­ aged to outperform the best system. An important part of this paper is detailed error analysis where we study the discrepancies between WiC predictions and human annotations and their effect on the LSCD results.
Artificial Intelligence in smart cities and urban mobility How can Artificial Intelligence applications be used in urban mobility and smart cities and how can their deployment be facilitated

The briefing analyses beneficial AI applications for smart cities and urban mobility, focusing on relevant use cases and challenges faced by the public sector when it comes to the uptake and deployment of such AI solutions. The briefing aims at providing valuable information to the AIDA special Committee, in order to enrich ongoing discussions concerning the difficulties faced by municipalities to deploy fundamental KEY FINDINGS
Applications of Ultrasound-Mediated Drug Delivery and Gene Therapy

Gene therapy has continuously evolved throughout the years since its first proposal to develop more specific and effective transfection, capable of treating a myriad of health conditions. Viral vectors are some of the most common and most efficient vehicles for gene transfer. However, the safe and effective delivery of gene therapy remains a major obstacle. Ultrasound contrast agents in the form of microbubbles have provided a unique solution to fulfill the need to shield the vectors from the host immune system and the need for site specific targeted therapy. Since the discovery of the biophysical and biological effects of microbubble sonification, multiple developments have been made to enhance its applicability in targeted drug delivery. The concurrent development of viral vectors and recent research on dual vector strategies have shown promising results. This review will explore the mechanisms and recent advancements in the knowledge of ultrasound-mediated microbubbles in targeting gene and drug therapy.
Research on Modeling and Control Method of DC-DC Converter

Since mankind entered the industrial society, DC-DC converters have been used in many industries. The function of the DC-DC converter is a module that converts DC voltage into DC voltage, which can also be called a power processing system. The main content of this article is to explain the basic principles of Buck converters, and perform modeling and simulation on the MATLAB platform to prove the feasibility of expectations.
Combining two national‐scale datasets to map soil properties, the case of available magnesium in England and Wales

Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant‐available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross‐validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties.
A new algorithm for a class of Distance Geometry problems

We propose a new algorithm for Discretizable Molecular Distance Geometry problems (DMDGPs), a class of Distance Geometry problems (DGPs) whose search space can be discretized and represented by a binary tree. By eﬃciently exploiting the many interesting symmetry properties of DMDGP instances, the new algorithm solves a sequence of nested and overlapped DMDGP subproblems rather than exploring the binary tree in a depth ﬁrst manner as the classic Branch-and-Prune (BP) algorithm. Computational results on artiﬁcially generated instances show that the new algorithm outperforms the classic BP algorithm in sparse DMDGPs.
A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applications in medication is a venue internationally, for medical trade and creating an energetic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxonomy of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain.