Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on the Extraction Methods of Crude oil from all Generation Biofuels in last few Decades

The ever growing demand for the energy fuels, economy of oil, depletion of energy resources and environmental protection are the inevitable challenges required to be solved meticulously in future decades in order to sustain the life of humans and other creatures. Switching to alternate fuels that are renewable, biodegradable, economically and environmentally friendly can quench the minimum thirst of fuel demands, in addition to mitigation of climate changes. At this moment, production of biofuels has got prominence. The term biofuels broadly refer to the fuels derived from living matter either animals or plants. Among the competent biofuels, biodiesel is one of the promising alternates for diesel engines. Biodiesel is renewable, environmentally friendly, safe to use with wide applications and biodegradable. Due to which, it has become a major focus of intensive global research and development of alternate energy. The present review has been focused specifically on biodiesel. Concerning to the biodiesel production, the major steps includes lipid extraction followed by esterification/transesterification. For the extraction of lipids, several extraction techniques have been put forward irrespective of the generations and feed stocks used. This review provides theoretical background on the two major extraction methods, mechanical and chemical extraction methods. The practical issues of each extraction method such as efficiency of extraction, extraction time, oil sources and its pros and cons are discussed. It is conceived that congregating information on oil extraction methods may helpful in further research advancements to ease biofuel production.
Artemisinin versus Nonartemisinin Combination Therapy for Uncomplicated Malaria: Randomized Clinical Trials from Four Sites in Uganda

Background Drug resistance in Plasmodium falciparum poses a major threat to malaria control. Combination antimalarial therapy including artemisinins has been advocated recently to improve efficacy and limit the spread of resistance, but artemisinins are expensive and relatively untested in highly endemic areas. We compared artemisinin-based and other combination therapies in four districts in Uganda with varying transmission intensity. Methods and Findings We enrolled 2,160 patients aged 6 mo or greater with uncomplicated falciparum malaria. Patients were randomized to receive chloroquine (CQ) + sulfadoxine-pyrimethamine (SP); amodiaquine (AQ) + SP; or AQ + artesunate (AS). Primary endpoints were the 28-d risks of parasitological failure either unadjusted or adjusted by genotyping to distinguish recrudescence from new infections. A total of 2,081 patients completed follow-up, of which 1,749 (84%) were under the age of 5 y. The risk of recrudescence after treatment with CQ + SP was high, ranging from 22% to 46% at the four sites. This risk was significantly lower (p < 0.01) after AQ + SP or AQ + AS (7%–18% and 4%–12%, respectively). Compared to AQ + SP, AQ + AS was associated with a lower risk of recrudescence but a higher risk of new infection. The overall risk of repeat therapy due to any recurrent infection (recrudescence or new infection) was similar at two sites and significantly higher for AQ + AS at the two highest transmission sites (risk differences = 15% and 16%, p< 0.003). Conclusion AQ + AS was the most efficacious regimen for preventing recrudescence, but this benefit was outweighed by an increased risk of new infection. Considering all recurrent infections, the efficacy of AQ + SP was at least as efficacious at all sites and superior to AQ + AS at the highest transmission sites. The high endemicity of malaria in Africa may impact on the efficacy of artemisinin-based combination therapy. The registration number for this trial is ISRCTN67520427 (http://www.controlled-trials.com/isrctn/trial/|/0/67520427.html).
Nowhere dense classes of graphs (characterisations and algorithmic meta-theorems) (Nowhere Dense Klassen von Graphen) (Charakterisierungen und algorithmische Meta-Sätze)

We show that every first-order property of graphs can be decided in almost linear time O(n1+ε) on every nowhere dense class of graphs. For graph classes closed under taking subgraphs, our result is optimal (under a standard complexity theoretic assumption): it was known before that for all classes C of graphs closed under taking subgraphs, if deciding first-order properties of graphs in C is fixed-parameter tractable, parameterized by the length of the input formula, then C must be nowhere dense. Nowhere dense graph classes form a large variety of classes of sparse graphs including the class of planar graphs, actually all classes with excluded minors, and also bounded degree graphs and graph classes of bounded expansion. For our proof, we provide two new characterisations of nowhere dense classes of graphs. The first characterisation is in terms of a game, which explains the local structure of graphs from nowhere dense classes. The second characterisation is by the existence of sparse neighbourhood covers. On the logical side, we prove a rank-preserving version of Gaifman’s locality theorem. The characterisation by neighbourhood covers is based on a characterisation of nowhere dense classes by generalised colouring numbers. We show several new bounds for the generalised colouring numbers on restricted graph classes, such as for proper minor closed classes and for planar graphs. Finally, we study the parameterized complexity of the first-order modelchecking problem on structures where an ordering is available to be used in formulas. We show that first-order logic on ordered structures as well as on structures with a successor relation is essentially intractable on nearly all interesting classes. On the other hand, we show that the modelchecking problem of order-invariant monadic second-order logic is tractable essentially on the same classes as plain monadic second-order logic and that the model-checking problem for successor-invariant first-order logic is tractable on planar graphs.
Journal of Integrative Bioinformatics

G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical research. Hence, there are a lot of investigations on GPCRs. Experimental laboratory research is very costly in terms of time and expenses, and accordingly, there is a marked tendency to use computational methods as an alternative method. In this study, a prediction model based onmachine learning (ML) approacheswas developed to predict GPCRs and ligand interactions. Decision tree (DT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Naive Bayes (NB) were the algorithms that were investigated in this study. After several optimization steps, receiver operating characteristic (ROC) for DT, RF, MLP, SVM, and NB algorithm were 95.2, 98.1, 96.3, 95.5, and 97.3, respectively. Accordingly final model was made base on the RF algorithm. The current computational study compared with others focused on specific and important types of proteins (GPCR) interaction and employed/ examined different types of sequence-based features to obtainmore accurate results. Drug science researchers could widely use the developed prediction model in this study. The developed predictor was applied over 16,132 GPCR-ligand pairs and about 6778 potential interactions predicted.
A wireless escape system for industrial accidents using Ad Hoc positioning

The safety of producing is a very important thing in the industry. But in some dangerous factories e.g. coal mines, circumstances are not allowed to deploy wires. So we bring in the ad hoc wireless sensor network to measure the key physical elements in the workshop. As soon as there are some signs of the accident or the accident has just happened, the system will know it and give the alarm to workers. Our system uses the AOA (angle of arrival) method with the RSSI (received signal strength indicator) filter to get the position of workers. And we propose an improved divisional Dijkstra algorithm to calculate the escape way for the first time to our acknowledgment.
Natural resource management for recreation development : a methodological approach for route and viewpoint planning in Taiwan

The objective of this study is to improve the balance between the protection of a natural resource base and recreation provision within a national park area. This management objective was accomplished by the integration of data from questionnaire surveys with regression modelling and the use of a Geographic Information System (GIS). A case study on natural resource and recreation planning was carried out in the Chitou Forest Recreation Area (which has national park status) in Taiwan. Data on the recreation preferences and demands of visitors were collected and investigated through questionnaires administered in the Park. Three areas were examined: the overall satisfaction with Park visits, the disincentives associated with crowd intensity and landscape component preferences. Visitor opinions were quantified and the relationship between variables and visitor preferences was investigated. The investigation involved the development of two regression based models, one dealing with overall satisfaction and the other with the impact of crowd intensity on visitor enjoyment. These models were combined with bio-physical and socioeconomic data from the Park including recreational resources, building costs, remoteness preferences and environmental sensitivity factors, and were utilised to plan a new path network system in the park. GIS was used to seek the best development solution which included maximising visitor satisfaction and minimising cost (both financial and ecological). At the GIS analysis stage, IDRISI Multiple Criteria Evaluation (MCE), a decision support tool for multiple objective planning was adopted for three development scenarios designed to meet the different considerations of park management. Having identified the best potential additional viewpoints, a new pathway through these favoured areas was designed from a start point and end point which joined with the current path system. By this methodological approach, recreation preferences were quantified and integrated with GIS, and applied into recreation planning. The new pathway design met the Parks’ recreation development objectives of low cost, abundant recreation resources, high satisfaction and ease of access.
Fast Computation of Graph Edit Distance

The graph edit distance (GED) is a well-established distance measure widely used in many applications. However, existing methods for the GED computation suffer from several drawbacks including oversized search space, huge memory consumption, and lots of expensive backtracking. In this paper, we present BSS_GED, a novel vertex-based mapping method for the GED computation. First, we create a small search space by reducing the number of invalid and redundant mappings involved in the GED computation. Then, we utilize beam-stack search combined with two heuristics to efficiently compute GED, achieving a flexible trade-off between available memory and expensive backtracking. Extensive experiments demonstrate that BSS GED is highly efficient for the GED computation on sparse as well as dense graphs and outperforms the state-of-the-art GED methods. In addition, we also apply BSS_GED to the graph similarity search problem and the practical results confirm its efficiency.
Multi-scale Adaptive Mask 3D Rigid Registration of Ultrasound and CT Images

3D Rigid intra-operative image registration is an important technique to provide guidance for pre-operative information from different image modalities. Due to the artefacts that cause correspondence ambiguity, accurate registration of US with other image modalities such as computed tomography (CT) is still a challenging problem. We propose a method which considers the registration problem of US and CT images as a multi-scale regional information saliency and local similarity selection process. We design our method as a multi-stage approach in which global and local rigid registrations alternate in each stage. During the local registration, the US image regions with high feature saliency and similarity with CT image will be selected and joined as region masks for the next global registration, other parts will not be considered in order to remove the correspondence ambiguity. The performances of our method are compared with a typical intensity based registration method on phantom and real patient images.
Rho Kinase Polymorphism Influences Blood Pressure and Systemic Vascular Resistance in Human Twins: Role of Heredity

The Rho/Rho kinase (ROCK) pathway is implicated in experimental hypertension. We, therefore, explored the role of ROCK2 genetic variation in human blood pressure (BP) regulation, exploiting the advantages of a human twin sample to probe heritability. The focus of this work is the common nonsynonymous variant at ROCK2: Thr431Asn. Cardiovascular and autonomic traits displayed substantial heritability (from ≈33% to 71%; P<0.05). The Asn/Asn genotype (compared with Asn/Thr or Thr/Thr) was associated with greater resting systolic (P<0.001), diastolic (P<0.0001), and mean BP (P<0.0001); allelic variation at ROCK2 accounted for up to ≈5% of BP variation (P<0.0001). Systemic vascular resistance was higher in Asn/Asn individuals (P=0.049), whereas cardiac output, large artery compliance, and vasoactive hormone secretion were not different. Coupling of the renin-angiotensin system to systemic resistance and BP was diminished in Asn/Asn homozygotes, suggesting genetic pleiotropy of Thr431Asn, confirmed by bivariate genetic analyses. The Asn/Asn genotype also predicted higher BP after environmental (cold) stress. The rise in heart rate after cold was less pronounced in Asn/Asn individuals, consistent with intact baroreceptor function, and baroreceptor slope was not influenced by genotype. Common genetic variation (Thr431Asn) at ROCK2 predicts increased BP, systemic vascular resistance (although not large artery compliance), and resistance in response to the endogenous renin-angiotensin system, indicating a resistance vessel-based effect on elevated BP. The results suggest that common variation in ROCK2 exerts systemic resistance-mediated changes in BP, documenting a novel mechanism for human circulatory control, and suggesting new possibilities for diagnostic profiling and treatment of subjects at risk of developing hypertension.
Sensor selection in energy harvesting wireless sensor networks

In this paper, we propose a novel energy harvesting (EH)-aware sensor selection policy. Our goal is to minimize the distortion in the reconstruction of the underlying source subject to the causality constraints imposed by the EH process at the sensor nodes. Besides, we determine the optimal power allocation for a given sensor selection (which admits a two-dimensional directional waterfilling interpretation) as the solution of an offline convex optimization problem. To that aim, we propose an iterative procedure. Performance is assessed by means of simulations and a lower bound that we also establish.
Hippocrates: A Context-aware, Collaboration Enabling Search Tool

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
A two-channel relay for autotuning

Tuning of feedback controllers for SISO systems often starts from information about the intersection of the Nyquist curve and the negative real axis. In standard autotuning this point is identified by a relay feedback experiment. For many processes, however, it would be advantageous to identify a point on the Nyquist curve somewhere in the third quadrant. The aim of this paper is two-fold. First, a noniterative procedure is suggested for identification of an arbitrarily chosen point in the third quadrant by the use of a two-channel relay. The two-channel relay consists of two relays operating in parallel on the process output and on the integral of the process output, respectively. Secondly, general a priori plant knowledge, often available, is utilized for the choice between PI and PID control and for amplitude-and-phase-margin based controller tuning.
Spatial scale dependency issues in the application of the Modified Universal Soil Loss Equation (MUSLE)

ABSTRACT The MUSLE is used within hydrological models to estimate sediment yields from catchments of various sizes, but the spatial scale dependency issues associated with estimating the MUSLE parameters have not been adequately addressed. In the absence of detailed observed data on both hydrological response and sediment yield, some analytical approaches and hypothetical examples are presented to identify the key issues. The results suggest that methods used to estimate both the erosivity and topographic factors are scale dependent, particularly if a lumped or semi-distributed modelling approach is used. The conclusion is that spatial scale dependencies will add to the uncertainties inherent in the use of the MUSLE if not carefully understood and appropriately addressed. One suggested approach is to apply the erosivity equation to a fixed (small) representative area and then scale up to the total catchment, an approach that recognizes the variability of averaged parameters across different spatial scales.
The New Development in Support Vector Machine Algorithm Theory and Its Application

As to classification problem, this paper puts forward the combinatorial optimization least squares support vector machine algorithm (COLS-SVM). Base d on algorithmic analysis of COLS-SVM and improves on it , the improved COLS-SVM can be used on individual credit evaluation. As to regression problem, appropriate kernel function and parameters were selected based on the analysis of support vector regression (SVR) algorithm. This paper proposes the forecasting model of coal mine ground-water-level based on SVR algorithm and improves on it . In another regression problem, it improves on successive overrelaxation for support vector regression (SORR) algorithm to measure the cholesterol content of a blood sample concerning the three kinds of plasma lipoproteins (VLDL, LDL, HDL) in medical science. The numerical experiment results show that the improved COLS-SVM algorithm and Mine Ground-water-level Forecasting improved Model and improved SORR algorithm are effective.
Compositional Features of Eukaryotic Genomes for Checking Predicted Genes

Gene prediction relies on the identification of characteristic features of coding sequences that distinguish them from non-coding DNA. The recent large-scale sequencing of entire genomes from higher eukaryotes, in conjunction with currently used gene prediction algorithms, has provided an abundance of putative genes that can now be analysed for their compositional properties. Strong, systematic differences still exist, in several species, between the compositional properties of sets of ex novo predicted genes and genes that have been experimentally detected and/or verified. This is particularly evident in the estimated gene set (>45,000 genes) of the recently sequenced rice genome, where roughly half the predicted genes are compositionally unusual and have no known orthologues in the dicot Arabidopsis. In a few cases such differences might suggest a bias in experimental gene-finding protocols, but the quasi-random nature of the compositionally aberrant predicted genes is a strong indication that many, if not most, of them are false positives. It therefore appears that some important features of coding regions have not yet been taken into account in existing gene prediction programs. Statistical base compositional properties of curated gene data sets from vertebrates, which we briefly review here, should therefore provide a useful benchmark for fine-tuning probabilistic gene models and model parameters that are currently in use.
Enhanced ApoA-I-dependent Cholesterol Efflux by ABCA1 from Sphingomyelin-deficient Chinese Hamster Ovary Cells*

ATP binding cassette protein A1 (ABCA1) plays a major role in cholesterol homeostasis and high density lipoprotein (HDL) metabolism. It is proposed that ABCA1 reorganizes the plasma membrane and generates more loosely packed domains that facilitate apoA-I-dependent cholesterol efflux. In this study, we examined the effects of the cellular sphingomyelin level on HDL formation by ABCA1 by using a Chinese hamster ovary-K1 mutant cell line, LY-A, which has a missense mutation in the ceramide transfer protein CERT. When LY-A cells were cultured in Nutridoma-BO medium and sphingomyelin content was reduced, apoA-I-dependent cholesterol efflux by ABCA1 from LY-A cells increased 1.65-fold compared with that from LY-A/CERT cells stably transfected with human CERT cDNA. Exogenously added sphingomyelin significantly reduced the apoA-I-dependent efflux of cholesterol from LY-A cells, confirming that the decrease in sphingomyelin content in the plasma membrane stimulates cholesterol efflux by ABCA1. The amount of cholesterol available to cold methyl-β-cyclodextrin (MβCD) extraction from LY-A cells was increased by 40% by the expression of ABCA1 and was 1.6-fold higher than that from LY-A/CERT cells. This step in ABCA1 function, making cholesterol available to cold MβCD, was independent of apoA-I. These results suggest that the function of ABCA1 could be divided into two steps: (i) a flopping step to move phosphatidylcholine and cholesterol from the inner to outer leaflet of the plasma membrane, where cholesterol becomes available to cold MβCD extraction, and (ii) a loading step to load phosphatidylcholine and cholesterol onto apoA-I to generate HDL.
Scalable Kernel Fusion for Memory-Bound GPU Applications

GPU implementations of HPC applications relying on finite difference methods can include tens of kernels that are memory-bound. Kernel fusion can improve performance by reducing data traffic to off-chip memory, kernels that share data arrays are fused to larger kernels where on-chip cache is used to hold the data reused by instructions originating from different kernels. The main challenges are a) searching for the optimal kernel fusions while constrained by data dependencies and kernels' precedences and b) effectively applying kernel fusion to achieve speedup. This paper introduces a problem definition and proposes a scalable method for searching the space of possible kernel fusions to identify optimal kernel fusions for large problems. The paper also proposes a codeless performance upper-bound projection model to achieve effective fusions. Results show that using the proposed scalable method for kernel fusion improved the performance of two real-world applications containing tens of kernels by 1.35x and 1.2x.