Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Alum Sludge from Surabaya Water Treatment Plant, Indonesia

A conventional water treatment plant (WTP) typically involves coagulation-flocculation processes to remove suspended particles and colloids in raw water. The process generates a large volume of alum sludge with high aluminum content, which is discharged into a river with improper treatment. The sludge may cause a potential risk to human health, and disrupt the life of river biota. The aims of this study were to determine the physical and chemical characteristics of alum sludge from Surabaya WTP, and to compare them with those of alum sludge from other plants in Indonesia and developing countries. The alum sludge sample was obtained from the Surabaya WTP in Indonesia. The results showed that the alum sludge had a pH value of 7.47, volatile solids of 12,696 mg/L, total suspended solids of 12,511 mg/L, chemical oxygen demand (COD) 9666.7 mg/L, biochemical oxygen demand (BOD) 1082.5 mg/L, and sludge volume index 114.18 mL/g. The sludge had high aluminum and iron concentrations. The aluminum content of the sludge was 1194 mg/L, iron 515 mg/L, chromium 0.217 mg/L, and copper 0.559 mg/L. Having a BOD/COD ratio of 0.1, the alum sludge contained high level of nonbiodegradable organic matter.
User-Centric Security and Dependability in the Clouds-of-Clouds

A promising vision of distributed cloud computing is a unified world of multiple clouds, with business benefits at hand. In practice, lack of interoperability among clouds and management complexity raise many security and dependability concerns. The authors propose secure Supercloud computing as a new paradigm for security and dependability management of distributed clouds. Supercloud follows a user-centric and self-managed approach to avoid technology and vendor lock-ins. In Supercloud, users define U-Clouds, which are isolated sets of computation, data, and networking services run over both private and public clouds operated by multiple providers, with customized security requirements as well as self-management for reducing administration complexity. The article presents the Supercloud architecture with a focus on its security infrastructure. The authors illustrate through several use cases the practical applicability of the Supercloud paradigm.
Liquid Membrane Technology For Precious Metals Recovery From Industrial Waste

A basic principle of the liquid membrane technology is reviewed by highlighting some interesting features of the system over other processes especially for metal separation and recovery from industrial wastes. Some of commercial liquid membrane extraction processes of precious metals are presented as examples of potential applications of the system. The advantages and drawbacks of the liquid membrane technology and the future research direction are also included.
Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification

In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.
Dielectric measurement: error analysis and assessment of uncertainty

The advantages and limitations of using partial differential analysis to assess the methodological uncertainty associated with the measurement of the dielectric properties of a material are discussed and an alternative pragmatic approach is proposed. It relies on repeat measurements of the dielectric properties of reference liquids and an analysis to estimate random and systematic uncertainties. Examples of measurement uncertainty are provided for well-defined monomolecular materials and for less homogeneous materials at microwave frequencies. All examples relate to measurement with an open-ended coaxial probe but the methodology is not specific to this technique. Examination of the components of uncertainty in the dielectric properties of biological tissue shows that, where the system is free of methodological bias, random fluctuations originating from sampling and natural inhomogeneity dominate the uncertainty budget. In such cases, the mean value of the measured parameter and the standard error of the mean can be taken as a good measure of the true value and its associated uncertainty.
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.
Defining the Structure-Function Relationships of Bluetongue Virus Helicase Protein VP6

ABSTRACT The VP6 protein of bluetongue virus possesses a number of activities, including nucleoside triphosphatase, RNA binding, and helicase activity (N. Stauber, J. Martinez-Costas, G. Sutton, K. Monastyrskaya, and P. Roy, J. Virol. 71:7220-7226, 1997). Although the enzymatic functions of the protein have been documented, a detailed structure and function study has not been completed and the oligomeric form of the protein in solution has not been described. In this study, we have characterized VP6 activity by creating site-directed mutations in the putative functional helicase domains. Mutant proteins were expressed at high levels in an insect cell by using recombinant baculoviruses purified and analyzed for ATP binding, ATP hydrolysis, and RNA unwinding activities. UV cross-linking experiments indicated that the lysine residue in the conserved motif AXXGXGK110V is directly involved in ATP binding, whereas mutant R205Q in the arginine-rich motif ER205XGRXXR bound ATP at a level comparable to that of the wild-type protein. The RNA binding activity was drastically altered in the R205Q mutant and was also affected in the K110N mutant. Helicase activity was altered in both mutants. The mutation E157N in the DEXX sequence, presumed to act as a Walker B motif, showed an intermediate activity, implying that this motif does not play a crucial role in VP6 function. Purified protein demonstrated stable oligomers with a ring-like morphology in the presence of nucleic acids similar to those shown by other helicases. Gel filtration chromatography, native gel electrophoresis, and glycerol gradient analysis clearly indicated multiple oligomeric forms of VP6.
Time-domain algorithms for harmonic bandwidth reduction and time scaling of speech signals

Frequency scaling of speech signals by methods based on short-time Fourier analysis (STFA), analytic rooting, and harmonic compression using a bank of filters, is a complex operation which requires a large amount of computation in a digital implementation. It is shown in this paper that, by incorporating pitch frequency information into a frequency-scaling process based on STFA, it is possible, to a good approximation, to perform this operation in the time domain with very few arithmetic operations (one multiplication and two additions per output sample, in most applications). The derivation of the time-domain harmonic scaling (TDHS) algorithms, selection of parameters, and, in particular, the determination of an appropriate weighting function used in the algorithms, as well as several potential applications, are detailed in the paper. Two proposed applications are discussed in greater detail. These are 1) a vocoder system which incorporates waveform coding of the frequency divided signal (by a factor of up to 3), and 2) a computer-based isolated-word recognition system in which all input utterances are compressed to the same duration at the preprocessing phase effecting an overall computation reduction by a factor of up to 3. Computer simulation results which demonstrate the TDHS algorithms' performance are included.
Mapping plant communities and understanding the landscape structure of coastal barrens using an unmanned aerial vehicle

Coastal barrens are landscape mosaics patchworks of plant communities that exist in harsh environmental conditions created by land-sea interactions and shallow soils. Many rare and uncommon species inhabit these ecosystems, making them a high priority for conservation. In Nova Scotia, coastal barrens are abundant along the coastline of the Halifax region. Little is known of the spatial distributions of plant communities that inhabit them and their overall landscape structure. The purpose of this study was to investigate the use of a UAV to map plant communities and to quantify the landscape structure of coastal barrens. First, high-resolution multispectral UAV imagery was evaluated to discriminate plant communities from three classification levels across three coastal barrens sites in Halifax, Nova Scotia: Chebucto Head, Prospect Bay, and Polly’s Cove. All plant communities were discriminated with 95% confidence except for one pair, showing that plant communities in the coastal barrens could be discriminated with a high level of confidence using UAV imagery. Next, UAV imagery was classified to produce detailed maps of plant communities for the three coastal barrens landscapes. Environmental factors, such as elevation, stream networks and wind exposure were also mapped to help understand landscape structure. Sites were dominated by shrublands and dwarf heath; however, many other types of communities co-occurred on these landscapes, including bogs, salt marshes, and tree islands. The most common plant community across the three sites was Gaylussacia baccata shrubland. Plant community patches varied in ii size, shape, abundance, and spatial distribution from one plant community type to another and in many cases from one site to another. Landscape patterns were driven by various combinations of environmental factors, including slope position, proximity to stream networks, elevation, and distance to coastline. Overall differences in landscape structure could be mostly explained by the degree of topographic heterogeneity of each landscape. UAVs are an excellent tool for mapping plant communities and quantifying landscape structure and this information is critical for informing land managers, conservation planners, and policy makers.
Clinicopathologic Implications of PIWIL2 Expression in Colorectal Cancer

Background There are no established reports about the expression of the Piwil gene, a subfamily of the Piwi gene involved in RNA silencing and self-renewal, in colorectal carcinomas. It is known that the degree of PIWIL2 expression is higher in colorectal carcinomas. But its clinicopathologic significance remains undetermined. This study reassessed the relationship between PIWIL2 expression and the clinicopathologic parameters in colorectal carcinomas. Methods An immunohistochemistry of PIWIL2 expression was done in 60 cases of colorectal carcinoma. This was followed by an analysis of the correlation between PIWIL2 expression and clinicopathologic features and a survival analysis. Results There were 44 cases (73.3%) where the degree of PIWIL2 expression was relatively higher. The high degree of PIWIL2 expression was significantly correlated with the lower degree of differentiation (p=0.039), deep invasion (p=0.019) and perineural invasion (p=0.027). The overall survival was longer in patients with the lower degree of PIWIL2 expression than in those with the higher degree of PIWIL2 expression. Conclusions Our results showed that the degree of PIWIL2 expression was relatively higher in colorectal carcinomas and it was significantly correlated with variable clinicopathologic indicators for a poor prognosis. This indicates that PIWIL2-positive cells contribute to the progression of colorectal cancer.
Which Distance Metric is Right: An Evolutionary K-Means View

It is well known that the distance metric plays an important role in the clustering process. Indeed, many clustering problems can be treated as an optimization problem of a criterion function defined over one distance metric. While many distance metrics have been developed, it is not clear that how these distance metrics can impact on the clustering/optimization process. To that end, in this paper, we study the impact of a set of popular cosine-based distance metrics on K-means clustering. Specifically, by revealing the common order-preserving property, we first show that K-means has exactly the same cluster assignment for these metrics during the E-step. Next, by both theoretical and empirical studies, we prove that the cluster centroid is a good approximator of their respective optimal centers in the M-step. As such, we identify a problem with K-means: it cannot differentiate these metrics. To explore the nature of these metrics, we propose an evolutionary K-means framework that integrates K-means and genetic algorithms. This framework not only enables inspection of arbitrary distance metrics, but also can be used to investigate different formulations of the optimization problem. Finally, this framework is used in extensive experiments on real-world data sets. The results validate our theoretical findings on the characteristics and interrelationships of these metrics. Most importantly, this paper furthers our understanding of the impact of the distance metrics on the optimization process of K-means.
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax

A framework is presented for unsupervised learning of representations based on infomax principle for large-scale neural populations. We use an asymptotic approximation to the Shannon's mutual information for a large neural population to demonstrate that a good initial approximation to the global information-theoretic optimum can be obtained by a hierarchical infomax method. Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases. As confirmed by numerical experiments, our method is robust and highly efficient for extracting salient features from input datasets. Compared with the main existing methods, our algorithm has a distinct advantage in both the training speed and the robustness of unsupervised representation learning. Furthermore, the proposed method is easily extended to the supervised or unsupervised model for training deep structure networks.
Example-Based Regularization Deployed to Face Hallucination

Regularization plays a vital role in ill-posed problems. A properly chosen regularization can direct the solution toward a better quality outcome. An emerging powerful regularization is one that leans on image examples. In this paper, we propose a novel scheme for face hallucination. We target specially the quality of highly zoomed outputs. Our work bases on the pyramid framework and assigns several high-quality candidate patches for each location in the degraded image. We definite a global MAP penalty function to reject all the problematic examples, and then reconstruct the desired image using the patch which is left after pruning. Experimental results demonstrate that our approach can get better resolution.
Complex Structures and Collective Dynamics in Networked Systems: A Tutorial

The study of complex networks and collective dynamics occurring in biological, social and technical systems has experienced a massive surge of interest both from academia and industry. Many of the results on the mechanisms underlying the self-organized formation of complex dynamic networks in natural and man-made systems have been derived based on a statistical physics perspective. In this tutorial, we provide a basic introduction to this perspective which will help attendees to benefit from the vast literature on self-organization and self-adaptation phenomena available in the fields of network science and complex systems. We cover basic models and abstractions for the study of static complex networks as well as dynamical processes like, e.g., information diffusion, random walks, synchronization or the propagation of cascading failures. We further introduce recent advances in the study of dynamic (social) networks and demonstrate how the resulting methods can be practically applied in the engineering of self-organizing and self-adaptive distributed systems and protocols.
Mini-Rank: A Power-EfficientDDRx DRAM Memory Architecture

Memory power consumption has become a severe concern in multi-core computer platforms. As memory data rate, capacity and bandwidth are being pushed higher and higher, the power consumption of memory systems becomes a significant part in the overall system power profile. Conventional memory systems do not provide an efficient mechanism for managing its power and performance tradeoff. We propose a novel mini-rank architecture for DDRx memories to reduce memory power consumption by breaking each DRAM rank into multiple narrow mini-ranks and activating fewer devices for each request. We also propose a heterogeneous mini-rank design to further improve the performance-power tradeoff for each workload based on its memory access behavior and bandwidth requirement. The evaluation results show that homogeneous mini-rank significantly reduces memory power with small performance loss. For instance, using four-core multiprogramming workloads, a x32 mini-rank configuration reduces memory power by 19.5 percent with 1.3 percent performance loss on average for memory-intensive workloads. Heterogeneous mini-rank further improves the balance between the performance and power saving. For instance, it reduces the memory power by up to 38.0 percent with an average performance loss of 2.4 percent, compared with a conventional memory system. In comparison, the x32 homogeneous mini-rank reduces memory power by up to 25.4 percent; while the x8 homogeneous mini-rank incurs performance loss by up to 19.3 percent. Furthermore, heterogeneous mini-rank achieves consistently good performance-power tradeoff for workloads made by programs of diverse memory access behavior and bandwidth requirement.
TaintDroid

Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32% performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering

A mean-square error lower bound for the discrete-time nonlinear filtering problem is derived based on the van Trees (1968) (posterior) version of the Cramer-Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly non-Gaussian, dynamical systems and is more general than the previous bounds in the literature. The case of singular conditional distribution of the one-step-ahead state vector given the present state is considered. The bound is evaluated for three important examples: the recursive estimation of slowly varying parameters of an autoregressive process, tracking a slowly varying frequency of a single cisoid in noise, and tracking parameters of a sinusoidal frequency with sinusoidal phase modulation.
Apolipoprotein A-I (apoA-I) and apoA-I mimetic peptides inhibit tumor development in a mouse model of ovarian cancer

We examined whether reduced levels of Apolipoprotein A-I (apoA-I) in ovarian cancer patients are causal in ovarian cancer in a mouse model. Mice expressing a human apoA-I transgene had (i) increased survival (P < 0.0001) and (ii) decreased tumor development (P < 0.01), when compared with littermates, following injection of mouse ovarian epithelial papillary serous adenocarcinoma cells (ID-8 cells). ApoA-I mimetic peptides reduced viability and proliferation of ID8 cells and cis-platinum–resistant human ovarian cancer cells, and decreased ID-8 cell-mediated tumor burden in C57BL/6J mice when administered subcutaneously or orally. Serum levels of lysophosphatidic acid, a well-characterized modulator of tumor cell proliferation, were significantly reduced (>50% compared with control mice, P < 0.05) in mice that received apoA-I mimetic peptides (administered either subcutaneously or orally), suggesting that binding and removal of lysophosphatidic acid is a potential mechanism for the inhibition of tumor development by apoA-I mimetic peptides, which may serve as a previously unexplored class of anticancer agents.
Le karst haut alpin du Kanin (Alpes juliennes, Slovénie-Italie). Etat des connaissances et données récentes sur le fonctionnement actuel et l'évolution plio-quaternaire des structures karstiques

Le Kanin est un karst haut-alpin des Alpes juliennes italo-slovenes, dont la surface fut modelee par les glaciers quaternaires qui ont gomme les heritages morphologiques tertiaires aujourd'hui discrets dans le paysage. La disposition structurale permet aux emergences slovenes de drainer une part importante des eaux infiltrees sur le versant italien, comme l'a montre un tracage recent L'etude de l'hydrodynamique et de la physico-chimie des eaux met en evidence des transferts extremement rapides en periode de fonte des neiges ou lors des orages violents, activant des trop-pleins spectaculaires, telle l'emergence de la Boka, jaillissant en une cataracte de plus de 100 m de hauteur. La zone noyee liee au barrage impermeable de la vallee de la Soca ne freine guere ces transits. Toutefois, elle contribue au soutien des etiages, en delivrant des eaux mineralisees ayant longuement sejourne en leur sein durant les etiages. La presence de reseaux souterrains profonds et tres developpes releve de la combinaison de facteurs favorables : calcaires epais et fractures, gradient altitudinal important, precipitations considerables. Des datations paleomagnetiques dans l'un des grands reseaux (Crnelsko brezno) mettent en evidence des sediments glaciaires datant du Pleistocene inferieur. Leur situation semble indiquer que ce reseau serait ante-quaternaire.