Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Local Hydrodynamics on High-Rate Activated Sludge Flocculation in Laboratory and Full-Scale Reactors

High rate activated sludge (HRAS) processes have a high potential for carbon and energy recovery from sewage, yet they suffer frequently from poor settleability due to flocculation issues. The process of flocculation is generally optimized using jar tests. However, detailed jar hydrodynamics are often unknown, and average quantities are used, which can significantly differ from the local conditions. The presented work combined experimental and numerical data to investigate the impact of local hydrodynamics on HRAS flocculation for two different jar test configurations (i.e., radial vs. axial impellers at different impeller velocities) and compared the hydrodynamics in these jar tests to those in a representative section of a full scale reactor using computational fluid dynamics (CFD). The analysis showed that the flocculation performance was highly influenced by the impeller type and its speed. The axial impeller appeared to be more appropriate for floc formation over a range of impeller speeds as it produced a more homogeneous distribution of local velocity gradients compared to the radial impeller. In contrast, the radial impeller generated larger volumes (%) of high velocity gradients in which floc breakage may occur. Comparison to local velocity gradients in a full scale system showed that also here, high velocity gradients occurred in the region around the impeller, which might significantly hamper the HRAS flocculation process. As such, this study showed that a model based approach was necessary to translate lab scale results to full scale. These new insights can help improve future experimental setups and reactor design for improved HRAS flocculation.
Telecommunication Systems Spectrum Sensing Via Recongurable Antennas : Fundamental Limits and Potential Gains

We propose a novel spectrum sensing technique in cognitive radio networks that provides diversity and capacity benefits using a single antenna at the Secondary User (SU) receiver. The proposed scheme is based on a recongurable antenna: an antenna that is capable of altering its radiation characteristics by changing its geometric configuration. Each configuration is designated as an antenna mode or state and corresponds to a distinct channel realization. Based on an abstract model for the recongurable antenna, we tackle two different settings for the cognitive radio problem and present fundamental limits on the achievable diversity and throughput gains. First, we explore the \to cooperate or not to cooperate" tradeoff between the diversity and coding gains in conventional cooperative and non-cooperative spectrum sensing schemes, showing that cooperation is not always beneficial. Based on this analysis, we propose two sensing schemes based on recongurable antennas that we term as state switching and state selection. It is shown that each of these schemes outperform both cooperative and non-cooperative spectrum sensing under a global energy constraint. Next, we study the \sensingthroughput" trade-o, and demonstrate that using recongurable antennas, the optimal sensing time is reduced allowing for a longer transmission time, and consequently better throughput. Moreover, state selection can be applied to boost the capacity of SU transmission. Corresponding Author: Ahmed Ibrahim
The CarLogo-51 Dataset

This paper presents the CarLogo-51 dataset used to evaluate the ImageWeb algorithm in [1]. The dataset is collected from the Internet, composed of 51 categories of images containing famous car logos, and could be combined with any sets of distractor images for large-scale near-duplicate Web image search. There are in total 11903 images in the dataset, all of which contain a unique kind of recognizable car logos, and the minimal number of images in one category is 200. We claim that we simulate a Web environment that every concept contains many instances, so that we could adopt affinity propagation methods, such as ImageWeb, to improve the image search quality significantly.
REMOVING LAVALIER MICROPHONE RUSTLE WITH RECURRENT NEURAL NETWORKS

The noise that lavalier microphones produce when rubbing against clothing (typically referred to as rustle ) can be extremely difﬁ-cult to automatically remove because it is highly non-stationary and overlaps with speech in both time and frequency. Recent breakthroughs in deep neural networks have led to novel techniques for separating speech from non-stationary background noise. In this paper, we apply neural network speech separation techniques to remove rustle noise, and quantitatively compare multiple deep network architectures and input spectral resolutions. We ﬁnd the best performance using bidirectional recurrent networks and spectral resolution of around 20 Hz. Furthermore, we propose an ambience preservation post-processing step to minimize potential gating artifacts during pauses in speech.
Device Design Assessment of GaN Merged P-i-N Schottky Diodes

Device characteristics of GaN merged P-i-N Schottky (MPS) diodes were evaluated and studied via two-dimensional technology computer-aided design (TCAD) after calibrating model parameters and critical electrical fields with experimental proven results. The device’s physical dimensions and drift layer concentration were varied to study their influence on the device’s performance. Extending the inter-p-GaN region distance or the Schottky contact portion could enhance the forward conduction capability; however, this leads to compromised electrical field screening effects from neighboring PN junctions, as well as reduced breakdown voltage. By reducing the drift layer background concentration, a higher breakdown voltage was expected for MPSs, as a larger portion of the drift layer itself could be depleted for sustaining vertical reverse voltage. However, lowering the drift layer concentration would also result in a reduction in forward conduction capability. The method and results of this study provide a guideline for designing MPS diodes with target blocking voltage and forward conduction at a low bias.
A New Approach to Modeling and Estimation for Pairs Trading

Pairs trading is an speculative investment strategy based on relative mispricing between a pair of stocks. Essentially, the strategy involves choosing a pair of stocks that historically move together. By taking a long-short position on this pair when they diverge, a profit will be made when they next converge to the mean by unwinding the position. Literature on this topic is rare due to its proprietary nature. Where it does exist, the strategies are either adhoc or applicable to special cases only, with little theoretical verification. This paper analyzes these existing methods in detail and proposes a general approach to modeling relative mispricing for pairs trading purposes, with reference to the mainstream asset pricing theory. Several estimation techniques are discussed and tested for state space formulation, with Expectation Maximization producing stable results. Initial empirical evidence shows clear mean reversion behavior in selected pairs’ relative pricing. PhD Candidate, Department of Accounting and Finance, Monash University Director of Research, Department of Accounting and Finance, Monash University School of Mathematical Sciences, Monash University
CWPC_BiAtt: Character-Word-Position Combined BiLSTM-Attention for Chinese Named Entity Recognition

Usually taken as linguistic features by Part-Of-Speech (POS) tagging, Named Entity Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we put forward a new comprehensive-embedding, considering three aspects, namely character-embedding, word-embedding, and pos-embedding stitched in the order we give, and thus get their dependencies, based on which we propose a new Character–Word–Position Combined BiLSTM-Attention (CWPC_BiAtt) for the Chinese NER task. Comprehensive-embedding via the Bidirectional Llong Short-Term Memory (BiLSTM) layer can get the connection between the historical and future information, and then employ the attention mechanism to capture the connection between the content of the sentence at the current position and that at any location. Finally, we utilize Conditional Random Field (CRF) to decode the entire tagging sequence. Experiments show that CWPC_BiAtt model we proposed is well qualified for the NER task on Microsoft Research Asia (MSRA) dataset and Weibo NER corpus. A high precision and recall were obtained, which verified the stability of the model. Position-embedding in comprehensive-embedding can compensate for attention-mechanism to provide position information for the disordered sequence, which shows that comprehensive-embedding has completeness. Looking at the entire model, our proposed CWPC_BiAtt has three distinct characteristics: completeness, simplicity, and stability. Our proposed CWPC_BiAtt model achieved the highest F-score, achieving the state-of-the-art performance in the MSRA dataset and Weibo NER corpus.
Capabilities of combination of ferroglukin , fospren il and 1 hamavit in correction of hemostasis functional stat

4 New-born calves are still often found with iron deficit. This state negatively influences their growth and 5 development to some extent due to hemostasiopathy development. In this connection great scientific 6 and practical significance for veterinary science and physiology belongs to the search of approaches 7 to effective correction of new-born calves’ hemostasiopathy in lack of iron conditions. It seemed to be 8 perspective to evaluate the degree of influence of traditionally applied at iron deficit ferroglukin in 9 combination with metabolism stimulators (fosprenil and hamavit) on new-born calves’ hemostasis 10 system indices. During our research it was established that new-born calves with iron deficit also have 11 decreased plasma antioxidant protectability, intensification of lipids’ peroxidation processes, increase 12 of thrombocytes’ hemostatic activity and system of blood coagulation system along with decrease of 13 vascular wall’s ability to bind it. In our study we found that combination of ferroglukin, fosprenil and 14 hamavit given to new-born calves with iron deficit showed improved plasma antioxidant and lipid 15 peroxidation activity. Also normalization of thrombocyte activity and positive dynamics of hemostasis 16 vascular and plasma components was observed. Iron deficit of new-born calves can be considered as 17 the model of hemostasis abnormality . With its help we can try different means and their combinations 18 to remove hemostasiopathy. Received results allow us to consider the usage of combination of 19 fosprenil and hamavit on the background of ferroglukin to be unsufficient for removal of 20 hemostasiopathy in new-born calves with iron deficit. 21
Newton's Shear Flow Applied to Infiltration and Drainage in Permeable Media

The paper argues that universal approaches to infiltration and drainage in permeable media that pivot around capillarity and that led to dual porosity, non-equilibrium, or preferential flow need to be replaced by a dual process approach. One process has to account for relatively fast infiltration and drainage based on Newton's shear flow, while the other one is responsible for storage and relatively slow redistribution of soil water by focusing on capillarity. Already Schumacher (1864) postulated two separate processes. However, Buckingham's (1907) and Richards' (1931) apparent universal capillary-based approach to flow and storage of water in soils dominated. The paper introduces the basics of Newton's shear flow in permeable media. It presents experimental support for the four presumptions of (i) sharp wetting shock fronts; (ii) that move with constant velocities; (iii) atmospheric pressure prevails behind the wetting shock front; (iv) laminar flow. It further discusses the scale tolerance of the approach, its relationship to Darcy's (1856) law, and its extension to solute transport.
Antioxidant, Antimicrobial, Cytotoxic, Larvicidal and Anthelmintic Activities and Phenolic Contents of Cyclamen alpinum

In this study, antioxidant, cytotoxic, larvicidal, antimicrobial and anthelmintic effects and phenolic contents of ethanol, methanol and acetone extracts of leaf and tuber parts of Cyclamen alpinum were investigated. DPPH, ABTS, β-carotene assays were carried out in antioxidant activity and total phenolic and flavonoid contents were tested in determination assay. 9 phenolic contents were determined by HPLC. Artemia salina was used in the cytotoxic effect. Larvicidal effect was investigated against Culex pipiens. Disc diffusion method was used in antimicrobial effect. The tuber part was found to be more toxic than the leaf part in the anthelmintic activity assay. The highest value obtained from the antioxidant activity experiment was observed with value of 86.73 ± 0.16 (%) in DPPH assay. The lowest LC50 value in larvicidal effect was determined 0.151 mg/mL after 72 hours. Consequently, there is need for further studies on the Cyclamen alpinum, which has the ability to fight against diseases such as cancer, cardiovascular diseases, etc.
Adverse Selection and Search Frictions in Corporate Loan Contracts

We provide empirical evidence of both (1) price dispersion and (2) credit rationing in the corporate loan market. We argue that these properties are caused by two factors: an adverse selection resulting from the information asymmetry between lenders and borrowers, and search frictions in matching borrowers with lenders. We develop a model of loan markets in which lenders post an array of heterogeneous contracts, then borrowers tradeoff terms of loan contracts and matching probability between themselves. We show that a unique separating equilibrium exists where each type of borrower applies to a certain type of contract.
Landscape of RNA polyadenylation in Ecoli Maes

Polyadenylation is thought to be involved in the degradation and quality control of bacterial RNAs but relatively few examples have been investigated. We used a combination of 5′-tagRACE and RNA-seq to analyze the total RNA content from a wild-type strain and from a poly(A)polymerase deleted mutant. A total of 178 transcripts were either upor down-regulated in the mutant when compared to the wild-type strain. Poly(A)polymerase up-regulates the expression of all genes related to the FliA regulon and several previously unknown transcripts, including numerous transporters. Notable down-regulation of genes in the expression of antigen 43 and components of the type 1 fimbriae was detected. The major consequence of the absence of poly(A)polymerase was the accumulation of numerous sRNAs, antisense transcripts, REP sequences and RNA fragments resulting from the processing of entire transcripts. A new algorithm to analyze the position and composition of post-transcriptional modifications based on the sequence of unencoded 3′-ends, was developed to identify polyadenylated molecules. Overall our results shed new light on the broad spectrum of action of polyadenylation on gene expression and demonstrate the importance of poly(A) dependent degradation to remove structured RNA fragments.
Evolution via Topic Modeling : An Analytic Study

Open-source projects continue to evolve resulted in so many versions. Managing, visualizing and understanding an evolving software system are challenging tasks. In this work, we apply Latent Dirichlet Allocation (LDA) to discover source code topics and study their evolution over multiple software versions. We apply LDA to all versions of the system together and then determine how the assignment metric evolves over time. We study the software evolution of two large open-source projects, JEdit and JHotDraw, over twelve versions. The results show that changes in topics across versions are due to actual software changes such as adding, updating, and removing features. Our work suggests that using LDA can open many paths in software evolution research.
Suitability of Clustering Algorithms for Crime Hotspot Analysis

-Crime analysis is a field that needs immediate attention due to the drastic increase of number of crimes. Most of the crimes, from the past experiences of the police, are said to be concentrated in some areas, called hotspots and keep on recurring. Clustering algorithms are best applied to crime analysis, but suitability of broad spectrum of clustering algorithm for an application is an issue to be addressed. In this paper we evaluate three clustering algorithms i.e. hierarchical clustering, k-means clustering and DBSCAN clustering with the intent of finding the best one suitable for crime hotspot analysis. Each one of the clustering algorithm evaluated here need inputs such as number of clusters, neighbour distance, minimum number of points etc. are needed by a cluster. The cluster similarity measure is the Euclidean distance. The results suggest that DBSCAN is much more suitable to crime hotspot analysis due to its inherent nature of being density driven.
Syddansk Universitet Global identification of hnRNP A 1 binding sites for SSO-based splicing modulation

Background: Many pathogenic genetic variants have been shown to disrupt mRNA splicing. Besides splice mutations in the well-conserved splice sites, mutations in splicing regulatory elements (SREs) may deregulate splicing and cause disease. A promising therapeutic approach is to compensate for this deregulation by blocking other SREs with splice-switching oligonucleotides (SSOs). However, the location and sequence of most SREs are not well known. Results: Here, we used individual-nucleotide resolution crosslinking immunoprecipitation (iCLIP) to establish an in vivo binding map for the key splicing regulatory factor hnRNP A1 and to generate an hnRNP A1 consensus binding motif. We find that hnRNP A1 binding in proximal introns may be important for repressing exons. We show that inclusion of the alternative cassette exon 3 in SKA2 can be significantly increased by SSO-based treatment which blocks an iCLIP-identified hnRNP A1 binding site immediately downstream of the 5’ splice site. Because pseudoexons are well suited as models for constitutive exons which have been inactivated by pathogenic mutations in SREs, we used a pseudoexon in MTRR as a model and showed that an iCLIP-identified hnRNP A1 binding site downstream of the 5′ splice site can be blocked by SSOs to activate the exon. Conclusions: The hnRNP A1 binding map can be used to identify potential targets for SSO-based therapy. Moreover, together with the hnRNP A1 consensus binding motif, the binding map may be used to predict whether disease-associated mutations and SNPs affect hnRNP A1 binding and eventually mRNA splicing.
Postgraduate Students’ Recruitment Strategies in Higher Education Institutions of Malaysia

Almost every higher education institution in the world is facing a tough challenge to recruit new students especially the talented ones due to intense competition in the local and global market. The institution needs to design effective marketing strategies that able to recruit students. The recruitment strategies in public higher education institutions can be classified into three main categories namely; non-active recruiting strategy, passive recruiting strategy, and active recruiting strategy. This study reveals that non-active recruitment strategies such as reputation of the university, and passive recruitment strategies such as advertisements have significant relationships with the talented postgraduate students’ recruitment. However, active recruitment strategies (i.e. travelling abroad to recruit students) were not significantly correlated to talented postgraduate recruitment. Research Universities (i.e. USM, UM, UKM, UPM and UTM), seem to have more quality students as compared to other non-Research Universities except for the UIAM. Thus to be more effective, the recruitment of postgraduate student strategies and practices in the public universities needs to be reviewed continuously since the needs and wants of the students are ever changing. Keywords: Recruitment strategy, marketing strategy, higher education institutions, quality students JEL Classifications: 123, M31
Object Retrieval with Deep Convolutional Features

Image representations extracted from convolutional neural networks (CNNs) outdo hand-crafted features in several computer vision tasks, such as visual image retrieval. This chapter recommends a simple pipeline for encoding the local activations of a convolutional layer of a pretrained CNN utilizing the well-known Bag of Words (BoW) aggregation scheme and called bag of local convolutional features (BLCF). Matching each local array of activations in a convolutional layer to a visual word results in an assignment map, which is a compact representation relating regions of an image with a visual word. We use the assignment map for fast spatial reranking, finding object localizations that are used for query expansion. We show the suitability of the BoW representation based on local CNN features for image retrieval, attaining state-of-theart performance on the Oxford and Paris buildings benchmarks. We demonstrate that the BLCF system outperforms the latest procedures using sum pooling for a subgroup of the challenging TRECVid INS benchmark according to the mean Average Precision (mAP) metric.
Isolation, identification and characterization of new luminous bacteria from Chah Bahar Port, southern marine habitat of Iran

Coastal region of Chah Bahar port, Sea of Oman, was screened for the presence of bioluminescence bacteria for the first time. Water samples were taken from surface and subsurface layers and immediately spread on nutrient seawater complete (SWC) agar. Luminous colonies were observed after an overnight incubation at 25°C. Among twenty luminous isolates, four of them were selected for preliminary bacterial identification based on morphological and physiological characteristics. 16S rRNA genes of selected bacteria were then sequenced in order to be submitted in GeneBank database as new strains and performing phylogenetic analysis. Four different submitted bacterial strains are as follow, Vibrio sp. Persian 1, Vibrio sp. Persian 2, Vibrio sp. Persian 3, and Vibrio sp. Persian 4 with accession numbers of KC505639, KC765088, KC765089, and KC896417, respectively. Light emission of isolated luminous bacteria was measured using luminometer. Vibrio sp. Persian 1was found as the best light emitter with counts per second/OD 600 nm equal to 10 × 10 6 RLU/Sec/OD. Isolated Vibrio species were tested for their ability to form biofilm. Vibrio sp. Persian 3 showed weak ability to produce biofilm while other species were considered as moderate biofilm producers.