Miracle-dz/UniFAR-FTUs huggingface dataset:
Removal of Cu , Cd , Zn , Ni and Fe from wastewater comparison of three different substrates used in model scaled constructed wetland

Different wastewaters contain metals that, unlike organic pollutants, are not degradable in the biological treatment system. Primary removal mechanisms can be achieved via sorption with a use of effective sorption media and precipitation processes which have been well demonstrated for metal removal also in wetland environment. The aim of the study was to test substrates like sand, peat and ceramics waste particles for removal of selected metal ions in a model-scaled constructed wetland (CW). The model comprised three large compartments where sand, peat-sand mixture or ceramics waste particles were exchanged in three successive experiments. In the compartment filled with sand 99.3 %, 97.8 %, 97.8 %, 97.0 %, and 32.2 % efficiency of metal removal for Cu, Cd, Pb, Zn, and Ni was achieved respectively. Fe leached out of the substrate in this experiment. In the compartment filled with the mixture of peat and sand was the efficiency of metal removal for Zn 99.8 %, Cu 99.3 %, Cd 98.0 %, Pb 97.9 %, Ni 95.8 %, and Fe 28.3 %, while in the compartment with ceramics the treatment efficiency reached for Cu 99.6 %, Pb 98.0 %, Cd 98.0 %, Fe 92.8 %, Zn 52.0 %, and for Ni 12.4 %. The substrates used in horizontal subsurface flow model expressed the capacity of efficient metal reduction from wastewater and indicate on the possibility of their application in a real-scale CW as a separate, cost-efficient pre-treatment or post-treatment step in the case of additional need of metal removal from the wastewater. KEy woRDS: Constructed wetland, Sand, Peat, Ceramics, Heavy metal, Substrate, Wastewater treatment Received: 8. 5. 2007. Accepted: 2. 8. 2007. 1 Maja Zupančič Justin Limnos Ltd., Company for Applied Ecology, Water Ecology Group, Podlimbarskega 31, SI 1000 Ljubljana, Slovenia E-mail: maja@limnos.si * corresponding author 2 Alenka Šajn Slak Limnos Ltd., Company for Applied Ecology, Water Ecology Group, Podlimbarskega 31, SI 1000 Ljubljana, Slovenia 3 Tjaša Griessler Bulc Limnos Ltd., Company for Applied Ecology, Water Ecology Group, Podlimbarskega 31, SI 1000 Ljubljana, Slovenia  Original scientific paper International Journal of Sanitary Engineering Research Vol. 2  No. 1/2008 29 Removal of Cu, Cd, Zn, Ni and Fe from wastewater comparison of three different substrates.  Zupančič Justin M, Šajn Slak A, Griessler Bulc T
The power of close relationships and audiences: Interpersonal closeness and payment observability as determi- nants of voluntary payments

Individual decision-making in Pay-What-You-Want settings is prone to social influence. Especially payment observability and the social relationship with other buyers during the payment decision are two important components of social influence. In practical applications of Pay-WhatYou-Want both phenomena often occur together while not being investigated yet for more than two types of social relationships. Thus, it is not clear how the presence of various types of social relationships influence voluntary payments and how they relate to payment observability. This study examines both drivers of social influence and investigates how payment observability (audience effect) and different types of social relationships (closeness effect) affect voluntary payments at the American Museum of Natural History. 1034 subjects participated in the study. I find that both, payment observability and interpersonal closeness, significantly increase payments. Voluntary payments are significantly higher if observed by other buyers and if visitors are surrounded by interpersonally close others. A high level of consistency between beliefs and behavior with increasing interpersonal closeness is discussed as potential explanation of the closeness effect. The study results are robustly confirmed in a replication study with 995 subjects. JEL classifications: C90; D01; D91; L11
An Examination of Ironic Effects in Air-Pistol Shooting under Pressure

This study aimed to test the incidence of ironic performance errors in elite air-pistol shooters. Previous research has revealed that, when shooters are anxious, avoidant instructions can cause ironic performance breakdown, especially in the unintended direction. Fifty-seven experienced air-pistol shooters were given specific instructions not to shoot to a certain part of a target, under low- and high-anxiety conditions, respectively. Results demonstrated that, when instructed not to shoot in a specific direction, anxious shooters did so a significant number of times. Interestingly, there was no difference in non-target non-ironic error, which provides specific support for Wegner’s theory of ironic processes of mental control in air-pistol shooting. Consequently, these findings illustrated that the combination of increased anxiety with avoidant instructions could lead to such unintended performance errors, called ironic error. Thus, understanding the mechanism of the anxiety-performance relationship may be a useful theoretical framework which could provide practical, instruction-based interventions to reduce susceptibility to ironic errors under pressure.
Interpreting and Controlling Linguistic Features in Neural Networks’ Representations

In recent years, Neural Networks proved their usefulness in computing and processing vector representations to solve various NLP tasks. The key objective of the thesis is to provide a better explanation of the neural network’s representation of language, also known as embed-dings. We hypothesize that the understanding of representations is necessary to improve the models and alleviate issues hindering models’ performances. We investigate the spatial distribution of vector representation and correlation with language features (syntax, lexicon, morphology) to answer which phenomena are encoded by a model. We aim to indicate which parts of the representation are relevant to a speciﬁc type of information. We propose a method that allows disentangling embedding spaces into parts that encode speciﬁc linguistic features. Our ﬁndings generalize to multiple diverse languages. In future work, we intend to analyze the encoding of higher-level linguistic features. We want to apply our ﬁndings to cope with the unwanted behaviors of language models, such as the unprovoked generation of toxic or biased texts.
UPDATING THE STUDY OF PUNISHMENT

Introduction 1171 I. Punishment and Social Science: What Works, What Doesn't, and Why 1172 A. Substitution Effects 1 173 B. Decision Framing 1180 C. Educational Impact of Criminal Law 1182 D. Inverse Sentencing Effect 1185 E. Impact of Social Control 1186 E. Punishment Practice and Legitimacy 1193 II. Punishment and Modernity: Computer Crimes 1197 III. What Does Punishment Mean? 1199 IV. Criminal Law and the Legislative Process 1204 Conclusion 1209
Phylogenetic and Mutation Analysis of the Venezuelan Equine Encephalitis Virus Sequence Isolated in Costa Rica from a Mare with Encephalitis

Venezuelan Equine Encephalitis virus (VEEV) is an arboviral pathogen in tropical America that causes lethal encephalitis in horses and humans. VEEV is classified into six subtypes (I to VI). Subtype I viruses are divided into epizootic (IAB and IC) and endemic strains (ID and IE) that can produce outbreaks or sporadic diseases, respectively. The objective of this study was to reconstruct the phylogeny and the molecular clock of sequences of VEEV subtype I complex and identify mutations within sequences belonging to epizootic or enzootic subtypes focusing on a sequence isolated from a mare in Costa Rica. Bayesian phylogeny of the VEEV subtype I complex tree with 110 VEEV complete genomes was analyzed. Evidence of positive selection was evaluated with Datamonkey server algorithms. The putative effects of mutations on the 3D protein structure in the Costa Rica sequence were evaluated. The phylogenetic analysis showed that Subtype IE-VEEV diverged earlier than other subtypes, Costa Rican VEEV-IE ancestors came from Nicaragua in 1963 and Guatemala in 1907. Among the observed non-synonymous mutations, only 17 amino acids changed lateral chain groups. Fourteen mutations located in the NSP3, E1, and E2 genes are unique in this sequence, highlighting the importance of E1-E2 genes in VEEV evolution.
Electroencephalography Theta/Beta Ratio Decreases in Patients with Severe Obstructive Sleep Apnea

Purpose: Accumulating evidence suggests that theta/beta ratio (TBR), an electroencephalographic (EEG) frequency band parameter, might serve as an objective marker of executive cognitive control in healthy adults. Obstructive sleep apnea (OSA) has a detrimental impact on patients’ behavior and cognitive performance while whether TBR is different in OSA population has not been reported. This study aimed to explore the difference in relative EEG spectral power and TBR during sleep between patients with severe OSA and non-OSA groups. Patients and Methods: 142 participants with in-laboratory nocturnal PSG recording were included, among which 100 participants suffered severe OSA (apnea hypopnea index, AHI > 30 events/hour; OSA group) and 42 participants had no OSA (AHI ≤ 5 events/h; control group). The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The demographic and polysomnographic characteristics, relative EEG spectral power and TBR of the two groups were compared. Results: It was found that the beta band power during NREM sleep and total sleep was significantly higher in the OSA group than controls (p < 0.001, p = 0.012, respectively), and the theta band power during NREM sleep and total sleep was significantly lower in the OSA group than controls (p = 0.019, p = 0.014, respectively). TBR during NREM sleep, REM sleep and total sleep was significantly lower in the OSA group compared to the control group (p < 0.001 for NREM sleep and total sleep, p = 0.015 for REM sleep). TBR was negatively correlated with AHI during NREM sleep (r=−0.324, p < 0.001) and total sleep (r=−0. 312, p < 0.001). Conclusion: TBR was significantly decreased in severe OSA patients compared to the controls, which was attributed to both increased beta power and decreased theta power. TBR may be a stable EEG-biomarker of OSA patients, which may accurately and reliably identify phenotype of patients.
Factors influencing Irish farmers’ afforestation intentions

Title þÿ F a c t o r s i n f l u e n c i n g I r i s h f a r m e r s a f f o r e s t a t i o n i n t e n t i o n s Authors(s) Duesberg, Stefanie; Upton, Vincent; Ní Dhubháin, Áine Publication date 2014-02 Publication information Forest Policy and Economics, 39 : 13-20 Publisher Elsevier Item record/more information http://hdl.handle. net/10197/9402 Publisher's statement þÿ T h i s i s t h e a u t h o r s v e r s i o n o f a w o r k t h a t w a s a c c e p t e d f o r p u b l i c a t i o n i n F o r e s t P o l i c y a n d Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Forest Policy and Economics (39 (February 2014)) DOI:10.1016/j.forpol. 2013.11. 004 Publisher's version (DOI) 10.1016/j.forpol. 2013.11. 004
KIT and PDGFR a mutations in 104 patients with gastrointestinal stromal tumors (GISTs): a population-based study

Background: The prognostic signiﬁcance of KIT or platelet-derived growth factor receptor a (PDGFR a ) mutations in gastrointestinal stromal tumors (GISTs) is still controversial. Patients and methods: In all, 104 patients were diagnosed with GISTs by KIT immunoreactivity; tumor DNA was sequenced for the presence of mutations in KIT exons 9, 11, 13 and 17 and in PDGFR a exons 12 and 18. Disease-free survival (DFS) was analyzed in 85 radically resected patients. Results: KIT mutations occurred in exon 11 (69), in exon 9 (11) and in exon 17 (1). PDGFR a mutations were detected in exon 18 (10) and in exon 12 (3). Ten GISTs were wild type. Exon 11 mutations were as follows: deletions in 42 cases and point mutations in 20 cases and insertions and duplications, respectively, in 2 and 5 cases. A better trend in DFS was evident for duplicated and point-mutated exon 11 KIT GISTs. There was a signiﬁcant association between PDGFR a mutations, gastric location and lower mitotic index. Moreover, PDGFR a -mutated GISTs seemed to have a better outcome. Conclusions: Point mutations and duplications in KIT exon 11 are associated with a better clinical trend in DFS. PDGFR a -mutated GISTs are preferentially localized in the stomach and seem to have a favorable clinical behavior.
Assessment of Appropriate Geometry for Thermally Efficient CO2 Adsorption Beds

Carbon capture is one of the recently raised technologies to mitigate greenhouse gas emissions. Adsorption was introduced as an energy-efficient carbon capture process, and the literature primarily shows the utilization of circular cross-sectional adsorption beds for this purpose. In this regard, this paper investigates different shapes of adsorbent beds to determine the thermal and adsorption uptake enhancements. Three geometries are considered: circular, square, and triangular cross-sectional beds. Mg-MOF-74 is used as an adsorbent, and numerical simulation is developed using a user-defined function coupled with ANSYS-Fluent. The results show that the triangular cross-sectional bed exhibits better adsorption capacity and thermal management compared to other beds. For example, the triangular cross-sectional bed shows 6 K less than the circular one during the adsorption process. It is recommended that the triangular cross-sectional bed be used for temperature swing adsorption when pumping power is not important. The square bed comes second after the triangular one with a lower pressure drop, suggesting such beds as good candidates for pressure swing adsorption. The square bed could be an excellent choice for compact beds when CO2 uptake and pumping power are both important.
Experimental Investigation On Air EntrainmentBelow Impinging Jets By Means OfVideo Observations And Image Processing

Air entrainment as a result of a water jet plunging into a pool of water was studied by means of visualization techniques. Image processing algorithms were used to analyze the recorded sequences of the phenomenon. Data regarding the penetration depth and bubble size distribution was gathered for different jet impact velocities and jet lengths. The accumulated results are further used to validate the models implemented in computational fluid dynamics (CFD) codes.
Pattern-based methods for Improved Lexical Semantics and Word Embeddings

Natural Language Processing (NLP) is a field of research that aims, on the one hand, to give computational answers to linguistic questions, and on the other hand, develop applications for language oriented tasks, such as machine translation, text summarization and question answering. These two goals share a fundamental question, namely how to represent semantics ; e.g., what is the meaning of the word dog, of expressions such as Red Herring or kick the bucket, or even more complex language structures. This question is linguistic (or even cognitive) in nature, while having direct empirical implications. Inside semantics, one of the most important subfields is lexical semantics, which studies the meaning or language utterances such as words or subwords. The most prominent method for representing the semantics of language utterances is constructing feature vectors. These methods, also called vector space models, date back to the early 1970s. Until recent years, vector space models built co-occurrence matrices, such that each word is directly represented by the other words with which it co-occurs. In the last few years, novel approaches, often referred to as word embeddings, were developed for this task. These models were by large based on neural network algorithms, and were able to obtain substantial improvements on various semantic tasks. This success has made word embeddings a very popular tool, and created a sense that these models represent the complete semantics of a given word. In this dissertation, I will show that despite their tremendous success, word embeddings suffer from several limitations. First, I will show that while state-of-the-art word embeddings are exceptionally well at capturing word association (e.g., “cup” is associated with “coffee”), they are far worse at capturing word similarity (e.g., “cup” is similar to “glass”). Second, I will demonstrate that they cannot distinguish between similar (“good”/“great”) and opposite words (“good”/“bad”). Third, I will show that while word embeddings are successful at capturing the semantics of nouns (e.g., “house”, “dog”), they are far less successful in capturing verb semantics (e.g., “run”, “walk”). In order to address these problems, I will present a set of pattern-based solutions. Lexico-syntactic patterns (e.g., “X such as Y”, “X is a Y”) are one of the most effective alternatives to word embeddings in the task of semantic representation. They have been shown useful for capturing a wide range of semantic relations, including synonymy, hyponymy (is-a), antonymy (opposite-of), and many other relations. I will show that integrating patterns into word embeddings can greatly alleviate their problems. I will start by demonstrating that pattern based methods can be superior
A Survey on Deep Learning Based IoT Approach for Precision Crop Suggestion

: Agriculture plays the main role in a country's economic development. In the recent agricultural practices, the variation in climatic phenomena affects the weather conditions in different regions and based on that the soil characteristics may also vary. This influences the crop to be sown for getting a better yield. In agriculture with the advent of new technology, farming practices are now converted into precision farming. It includes the use of modern technology such as the Internet of Things (IoT) and Data Analytics for optimal crop health and crop productivity. This improves the growth rate of the crops, but the problem among farmers is they are not choosing the right crop at right time. Although several algorithms are there the Deep Learning based Artificial Neural Networks is found to be more effective for prediction and modeling. The algorithm accuracy and the prediction level vary based on the type of parameters chosen. The proposed system helps the farmers by gathering information about the basic characteristics of their soil such as soil moisture level, temperature, pH, and humidity. In addition, the sensor provides valuable information about crops such as sowing time, fertilizer suggestion and real-time monitoring. These data can be collected using sensors and with the help of that data, the Deep Learning technique such as Deep Neural Network (DNN) which is an Artificial Neural Network (ANN) can be applied to end up with valuable decision making.
Correlation Dimension in Sumatra Island Based on Active Fault, Earthquake Data, and Estimated Horizontal Crustal Strain to Evaluate Seismic Hazard Functions (SHF)

: This study intends to evaluate the possible correlation between the correlation dimension (D C ) and the seismic moment rate for different late Quaternary active fault data, shallow crustal earthquakes, and GPS on the island of Sumatra Probabilistic Seismic Hazard Analysis (PSHA). The seismicity smoothing was applied to estimate the D C of active faults (D F ) and earthquake data (D E ) and then to correlate that with the b-value, which will be used to identify seismic hazard functions (SHF) along with the Sumatra Fault Zone (SFZ). The seismicity based on GPS data was calculated by the seismic moment rate that is estimated based on pre-seismic horizontal surface displacement data. The correlation between D F , D E , and the b-value was analyzed, and a reasonable correlation between the two seismotectonic parameters, D F -b, and D E -b, respectively, could be found. The relatively high D C coincides with the high seismic moment rate model derived from the pre-seismic GPS data. Furthermore, the SHF curve of total probability of exceedance versus the mean of each observation point’s peak ground acceleration (PGA) shows that the relatively high correlation dimension coincides with the high SHF. The results of this study might be very beneﬁcial for seismic mitigation in the future.
E FFECTIVE F ACTORS ON CONTINUED USE OF THE P ICTURE A RCHIVING AND C OMMUNICATION S YSTEM IN T EACHING H OSPITALS OF S HIRAZ U NIVERSITY OF M EDICAL S CIENCES

Also, there was a significant relationship between the level of education of users and the continuance intention to use the PACS system (P=0.008). Radiologists have the highest tendency to continue using the PACS system and the least-favored were specialists. Conclusion: The results indicated that for continued use of information systems by users and increase their satisfaction and the success of systems, consideration of users' expectations, requirements and technical requirements of systems to fit the system with the tasks of users before implementing information systems is necessary and inevitable.
Temporally Dependent Mappings Between fMRI Responses and Natural Language Descriptions of Natural Stimuli

Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock [4], and learn bidirectional mappings between fMRI responses and natural language representations. We show how to leverage data from multiple subjects watching the same movie to improve the accuracy of the mappings, allowing us to succeed at a scene classification task with 72% accuracy (random guessing would give 4%) and at a scene ranking task with average rank in the top 4% (random guessing would give 50%). The key ingredients are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA [5, 24] to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature [3] that produces semantic vector representation of the annotations; (c) interpretably using previous timestep information in the featurization of the predictor data.
Application Scope of ZnSe-In 2 S 2 as a Dual Buffer Layer as a Substitute of CdS Buffer Layer in CIGS Based Solar Cell

:- Copper indium gallium selenide (CIGS) absorber based solar cell generally use CdS as a buffer layer. In large scale utilization of this kind of solar cell there is a debate on the CDS which comprises with toxic Cd. In this research different non-toxic materials have been studied observing the suitable energy band bending, potential of light insertion on the absorber layer and other electrical output parameters for optimum efficiency. From the research it has been revealed that out of single layer ZnS, ZnSe, ZnTe, SnO 2 , In 2 S 2 and double layerZnSe-In 2 S 2, ZnS-CdS materials the double layer ZnSe-In 2 S 2 has an excelled impact on the band alignment and shows an efficiency around the conventional single layer CdS based CIGS solar cells, ~21%. Hence this double layer ZnSe-In 2 S 2 can be an alternative of the toxic Cd based CdS in CIGS solar cells.