Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF pH AND SODIUM CHLORIDE CONCENTRATION ON THE FUNCTIONAL PROPERTIES OF LOCUST BEAN PULP FLOUR

The effects of pH and NaCl concentration on the func tio al properties of African locust bean pulp flour were investigated. Flour was prepared from locust bean p ulp. The pH of the flour was adjusted with HCl or Na OH solution from 2 12. The NaCl solution was added to the flour from 0.1 to 1.0M. The functional propert i s of the flour samples were evaluated. The locust bean p ulp flour had minimum nitrogen solubility of 3% at pH 4.0. At pH 2.0, the nitrogen solubility was 7 % and i creased to 10 % at pH 8 and thereafter, decrease d. The locust bean pulp flour had maximum water absorption capacity of 321% at pH 2.0 and minimum of 292% at pH 12. The oil absorption capacity increased from 2 57% in water to 262% at pH 2.0; dropped to 250% at pH 4.0 and increased to maximum 270% at pH 12. The loc ust bean pulp flour had minimum foaming capacity of 7.5% at pH 4.0 and maximum of 22% at pH 8.0. The ad dition NaCl up to 0.2 M enhanced the nitrogen solubility, foaming capacity and foam stability of l cust bean pulp flour but these parameters decreas ed t concentrations above 0.2M. The addition of NaCl up t o 0.3 M improved the water absorption capacity and oil absorption capacity of locust bean pulp flour. The addition of up to 0.4M increased the emulsion activ ity and emulsion stability of locust bean pulp flour. Sodi um chloride improved the emulsion activity in the p H range of 2-8. At any level of flour concentration, the fl our gelled at 0.2M NaCl concentration. At 0.4 and 0. 6 M NaCl concentrations, the least gelation concentratio ns were 8 and 16%, respectively. The locust bean pu lp flour gelled at 16% and above at any NaCl concentrat ion.
IRIT at e-Risk Idriss

In this paper, we present the method we developed when participating to the e-Risk pilot task. We use machine learning in order to solve the problem of early detection of depressive users in social media relying on various features that we detail in this paper. We submitted 4 models which differences are also detailed in this paper. Best results were obtained when using a combination of lexical and statistical features.
Elevational Gradients of Diversity and Species Composition of Hemerodromiinae ( Diptera : Empididae ) at Doi Inthanon , Thailand : Has Historical Partitioning between Seasonally Dry Lowland and Aseasonal Moist Mountain Forests Contributed to the Biodiversity of Southeast Asia ?

– Community structure, species richness and diversity of Hemerodromiinae (Diptera: Empididae) were investigated along an altitudinal gradient between 700 m and 2,500 m above mean sea level on the Doi Inthanon mountain in northern Thailand (18o 35’ N, 98o 29’ E). The diversity and richness were maximal at the upper-mid to sub-summit elevations corresponding to speciose communities associated with continually moist montane evergreen forests. A relatively depauperate species assemblage was associated with seasonally water-stressed biotopes at lower elevations. Relaxation of seasonal aridity at higher elevations was considered an important determinant of species richness and abundance for Hemerodromiinae, especially Chelipodini which require moist soils for development. Elevational patterns of strictly aquatic Hemerodromiini were reasoned to arise from geometrical constraints limiting the number of suitable streams at altitudinal extremes.there are more water courses at intermediate altitudes where abundance and diversity are greatest. Historical factors underlying current diversity and abundance patterns are discussed and hypothesised to involve three elements. (1) Historically concurrent orogenesis of mountain ranges, development of a seasonally arid monsoon climate in Southeast Asia and dry periods associated with glacial maxima forced altitudinal migrations into and subsequent radiation in aseasonal moist forests that developed on nascent mountains. (2) Immigration from the northwest and south along ‘corridors’ of montane moist forest, and (3) dispersal of lowland forms inhabiting seasonally dry biotopes in response to climatic drying.
Features Exploration of Distinct Load Balancing Algorithms in Cloud Computing Environment

------------------------------------------------------------------- ABSTRACT --------------------------------------------------------------- The delivery of Cloud computing is a method for delivering information /services in which resources are retrieved from the Internet through web-based tools and applications, as opposed to a direct connection to a server rather than keeping files on a proprietary hard drive or local storage device. We have studied a lot of algorithm for reducing the response time of load balancing algorithm in cloud computing environment. We also measured the execution time of different algorithms. In this paper we will compare execution time, processing time of data center, response time of various algorithms. --------------------------------------------------------------------------------------------------------------------------------------------------
ADAPTIVE CARDINAL UTILITY

Recent research in neuroscience inspires a foundation for a cardinal utility function that is adaptive and perhaps hedonic. We model adaptation as arising from a limited capacity to make fine distinctions, where the utility functions adapt in real time. We give the noisy nature of choice a central role. The mechanism adapts to an arbitrary distribution in a way that is approximately optimal, in terms of minimizing the probability of error. The model predicts the socalled “hedonic treadmill” and sheds light on national happiness measures.
Impact of deoxynivalenol on rainbow trout: Growth performance, digestibility, key gene expression regulation and metabolisation Running title: Impact of deoxynivalenol on rainbow trout

The impact of deoxynivalenol (DON) on rainbow trout, Onchorynchus mykiss, is mainly characterised by impaired growth performance and reduced feed intake, usually with the total absence of any visible clinical signs. Despite the high concentrations of DON in the present study (up to 11,412 ± 1,141 μg/kg), no clinical signs (except anorexia at the higher DON dosage) were observed, which confirms the difficulties of diagnosing DON ingestion. Compared to the control group, the proteolytic enzyme activities (pepsin, trypsin and chymotrypsin) in trout were altered by DON ingestion. However, it was not clear if the observed impact on digestive enzymes was due to the direct action of DON, or a consequence of the lower feed intake determined for DON-treated animals. The impact of DON on the abundance of specific measured mRNA transcripts was unexpected with higher expression levels for insulin-like growth factors, igf1 and igf2, which are directly related to elevated insulin levels in plasma. This can also in part be influenced by the trypsin activity and by npy, given its higher mRNA expression levels. The apparent digestibility of dry matter, protein and energy was not affected by dietary levels of DON, however, nutrient retention, protein, fat and energy retention were significantly affected in animals fed DON. Adenylate cyclase-activating polypeptide (PACAP) expression seems to play an important role in controlling feed intake in DON fed trout. In the present study, we have shown for the first time that DON is metabolised to DON-3-sulfate in trout. DON-3-sulfate is much less toxic than DON, which helps to explain the lack of clinical signs in fish fed DON. Being a novel metabolite identified in trout makes it a potential biomarker of DON exposure. Suppression of appetite due to DON contamination in feeds might be a defence mechanism in order to decrease the exposure of the animal to DON, therefore reducing the potential negative impacts of DON.
Can machine learning find extraordinary materials?

One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is indeed the case, this work takes advantage of density functional theory calculated properties (bulk modulus, shear modulus, thermal conductivity, thermal expansion, band gap and Debye temperature) to investigate whether machine learning is truly capable of predicting materials with properties that extend beyond previously seen values. We refer to these materials as extraordinary, meaning they represent the top 1% of values in the available data set. Interestingly, we show that even when machine learning is trained on a fraction of the bottom 99% we can consistently identify 3/4 of the highest performing compositions for all considered properties with a precision that is typically above 0.5. Moreover, we investigate a few different modeling choices and demonstrate how a classification approach can identify an equivalent amount of extraordinary compounds but with significantly fewer false positives than a regression approach. Finally, we discuss cautions and potential limitations in implementing such an approach to discover new record-breaking materials.
FORWARD-DYNAMICS TRACKING SIMULATION OF COUPLED MULTIBODY AND FINITE ELEMENT MODELS: APPLICATION TO THE TONGUE AND JAW

Finite-element (FE) models of muscle tissue are gaining popularity in musculoskeletal simulation research. In contrast to the traditional line-based Hill-type models, volumetric representations permit a detailed analysis of internal tissue deformation, contact conditions, and force transmission in broadly inserting muscles [1]. However, analysis techniques for FE models are less developed than those for traditional line-based muscles. One technique that has achieved widespread use in multibody musculoskeletal simulators (e.g. OpenSim [2]) is Computed Muscle Control (CMC) [3]. CMC predicts muscle excitations required for a forward-dynamics simulation to track a target kinematic trajectory. While CMC tracking-based simulation has been applied to strand-based musculotendon models [4], quasi-static FE-based muscles for the face [5] and line-based muscles for the tongue [6], it has yet to be applied to dynamically coupled rigid-body and FE muscle models. Doing so is needed for validating and applying volumetric muscle models in dynamic settings. We have extended the CMC algorithm for use with fully-coupled rigid skeletal bodies and FE-based muscle models. Our approach offers improved accuracy and convenience over the more standard practice of using multibody skeletal simulation to prescribe boundary conditions for use with a separate FE simulator (e.g. FEBio [7]). Our trackingbased simulation allows for flexible specification of targets, but nominally permits skeletal kinematics as input and FE-muscle activations and deformations as output. We demonstrate our approach with a jaw-tongue model (Figure 1) that tracks electro-magnetic midsagittal articulographic (EMMA) recordings of tongue and jaw movements in speech.
The Multi-Dimensional Nature of Standardisation in Support of Innovation : A Systematic Analysis of the History of Photovoltaic Technology

Despite increasing awareness of the critical role of standards in technological innovation, there remain significant challenges to managing standardisation activities in a timely and effective manner. This is due to limited understandings of complex dynamics between standardisation and innovation; different types of standards, developed by different sets of innovation actors, support a variety of innovation activities, at different stages of technological and industrial lifecycles. In order to overcome these challenges, recent theory-building efforts have led to the development of a novel systematic framework which carefully articulates and characterises important dimensions of standardisation in the context of technological innovation. Testing and building on this framework, this paper presents a systematic analysis of the history of photovoltaic (PV) technology and relevant standardisation. This case study not only demonstrates the importance of the multi-dimensional nature of standardisation in understanding how they support innovation, but also identifies a number of interesting patterns and trends, increasing our understandings of complex dynamics between standardisation and innovation. In addition, the case study provides additional insights for minor improvements of the framework itself. Capturing important aspects and issues to be considered for strategic management of standardisation, the refined framework is expected to support standards organisations and policymakers for more systematic and future-oriented analyses of standardisation in support of overall technological innovation.
A Sampling Approach for the Exploration of Biopolymer Energy Land

The folding of biopolymers is crucially determined by the properties and the topology of the underlying energy landscape. A reduced representation of these energy landscapes is provided by barrier trees, which can be used to study the dynamical behavior of the folding. We presented a generic, problem-independent approach for the generation of barrier trees of discrete biopolymer models. In contrast to previous studies, the approach used does not rely on enumeration, which is limited to smaller molecules due to the amount of available memory. The algorithm has been applied to RNA and a lattice protein. The results show that the approach can be used to compute both all local minima and the exact barrier tree of an energy landscape. The presented method does not restrict the investigated conformation space to certain regions.
Classification of Biochemical and Biomechanical Data of Diabetic Rats Treated with Magnetic Field By PCA-Supported J 48 Algorithm

The aim of this study was to investigate the J48 mediated decision tree algorithm from the principal component analysis PCA, which is more complex, one of the statistical algorithms of diabetic metabolic disorders of Wistar albino rats’ biochemical values and magnetic field application. Wistar Albino rats were examined under 4 different groups including the control group. When the results were examined, it was observed that PCA increased the success rate of classification from 96.25% to 97.50% when used with J48 decision tree algorithm. Thus, the PCA-supported J48 algorithm demonstrated that Wistar albino rats could be successfully used on the data obtained from more complex diabetic metabolic values.
NEW EVIDENCE ON THE DIRECT AND INDIRECT INFLUENCE OF THE MAQASHID SHARIA INDEX ON THE ISLAMIC SOCIAL REPORTING INDEX

Empirical research on the maqashid sharia index is still limited in number. The novelty offered in this study lies in the research model. The model developed in this study is maqashid sharia-based Islamic social reporting. This study aims to obtain empirical evidence regarding the influence of the maqashid sharia index on the index of Islamic social reporting and the level of tax aggressiveness. In addition, this study also aims to obtain empirical evidence regarding the indirect influence of the maqashid sharia index on Islamic social reporting through the level of tax aggressiveness. The sample of this study is eleven Islamic banks in Indonesia during the period of 7 years (2010-2016). Data were analyzed using the Partial Least Square (PLS) approach. The results of this study indicate that the higher the maqashid sharia index the wider Islamic social reporting conducted by Islamic banks. Furthermore, the results of the study also found a negative influence on the maqashid sharia index on the level of tax aggressiveness. On the contrary, the results of the study failed to prove the indirect influence of the maqashid sharia index on the index of Islamic social reporting through the level of tax aggressiveness. The results of the research have an important contribution in expanding the theory of legitimacy, the concept and role of the maqashid sharia index. Aside from being a performance measurement tool, based on the results of this study shows that the maqashid sharia index can play a determinant role in the extent of Islamic social reporting and the level of tax aggressiveness in sharia commercial banks in Indonesia.
Proposal of an Energy Comparison System in the SPT

This paper describes a mechanical device to compare the energy transference from the blow of a hammer to the stem referring to standardized and non-standardized equipment in Brazil. The device allows to measure the vertical displacement of a sleeve due to one or more blows of a hammer falling freely. Tests were carried out using three pieces of equipment for the SPT measurements, but only one was mounted on a tripod standardized by the NB 6484:2001 norm. Based on the displacement values, a comparative analysis of the available energies of the equipment was conducted. The efficiency of the standard tripod for the test performed without a strict control of the fall of the hammer relative to the transfer efficiency of the test performed with a strict control of the fall height was 82.5%, showing a significant influence of human factor on the results. The lowest coefficient of variation of the displacements (5.05%) was obtained for the test using mechanized equipment with an automatic hammer. From the standpoint of the available energy of the system, it is possible to use non-standard equipment by applying the correction factor (Cf) to SPT results. Finally, static tests were performed on the sleeve and the energy transferred to the system was calculated.
Probabilistic Priors-Based Artifacts Removal Filter-Based Artifacts Removal DL-Based Artifacts Removal DL-Based Loop Filter 2019 PRN

We present a comprehensive study and evaluation of existing single image compression artifacts removal algorithms, using a new 4K resolution benchmark including diversified foreground objects and background scenes with rich structures, called Large-scale Ideal Ultra high definition 4K (LIU4K) benchmark. Compression artifacts removal, as a common postprocessing technique, aims at alleviating undesirable artifacts such as blockiness, ringing, and banding caused by quantization and approximation in the compression process. In this work, a systematic listing of the reviewed methods is presented based on their basic models (handcrafted models and deep networks). The main contributions and novelties of these methods are highlighted, and the main development directions, including architectures, multi-domain sources, signal structures, and new targeted units, are summarized. Furthermore, based on a unified deep learning configuration (i.e. same training data, loss function, optimization algorithm, etc.), we evaluate recent deep learning-based methods based on diversified evaluation measures. The experimental results show the state-of-the-art performance comparison of existing methods based on both full-reference, non-reference and task-driven metrics. Our survey would give a comprehensive reference source for future research on single image compression artifacts removal and inspire new directions of the related fields.
Application of weighted gene co-expression network analysis to rheumatoid arthritis

The goal of this study was to identify hub genes as potential targets in rheumatoid arthritis (RA) using weighted gene co-expression network analysis (WGCNA). Gene expression profiles of GSE17755 were downloaded from the GEO database and screened for differentially expressed genes (DEGs) with the limma package in R. Significant modules in the network were identified via WGCNA. Then, Gene Ontology (GO) functional enrichment of genes in the most significant module was analyzed using Database for Annotation, Visualization, and Integrated Discovery (DAVID). Finally, the disease-related gene co-expression network was visualized using Cytoscape, and hub genes were identified on CytoHubba. Overall, 3666 DEGs and 8 modules were identified. The turquoise module including 1044 genes was identified as the most relevant to RA. GO functional enrichment showed genes in the most relevant module were mainly related to the inflammatory response and the type I interferon signaling pathway. Ten hub genes, including PIGL, PRKAA1, and MRPS10, were identified. Genes related to the inflammatory response and the type I interferon signaling pathway possibly play critical roles in RA pathogenesis. PIGL, PRKAA1, and MRPS10 may be new targets for treating RA.
UvA-DARE ( Digital Academic Repository ) Differential diagnosis in the memory clinic : Exploring the value of improved neuropsychological examination

Background: Studies investigating relations between cognitive performance and brain atrophy in patients with mild cognitive impairment (MCI) have been conducted in highly selected samples. This methodological strictness does however not reflect clinical practice, and limits the generalizability of these studies to the MCI population at large. Objective: To identify the cerebral morphometric correlates of cognitive deficits in a large, unselective sample of elderly patients referred to memory clinics. Methods: Participants (N=126) underwent structural, high-resolution Magnetic Resonance Imaging (MRI) and a comprehensive neuropsychological examination evaluating several cognitive domains. Whole-brain voxel-based morphometry was performed to examine relationships of cognitive functioning with regional grey matter (GM) volume. Results: Significant correlations were found between cognitive functioning, irrespective of cognitive domain, and regional GM volume in the temporal lobes, in particular the bilateral middle temporal gyri. Cognitive function in non-memory domains was correlated with regional brain morphology outside the temporal lobes only when studied in isolation. The primary memory impairment, characteristic for MCI patients, remained specifically related to lower volume of the left hippocampus, even when variance in other cognitive domains was accounted for. Conclusion: Our results suggest that correlations of volumetric data and cognitive data in memory clinic patients reflect the primary neurodegenerative pathology, i.e. temporal lobe atrophy. The relation between impaired memory and temporal lobe atrophy is much stronger in these patients than other brain-cognition relationships. Anne_brains_proefschrift_2_corr_NOBASE_2. indd 80 24-11-14 14:38 81 Exploring the value of improved neuropsychological examination
Speaker statistical averageness modulates word recognition in adverse listening conditions

We tested whether statistical averageness (SA) at the level of the individual speaker could predict a speaker’s intelligibility. 28 female and 21 male speakers of Dutch were recorded producing 336 sentences, each containing two target nouns. Recordings were compared to those of all other same-sex speakers using dynamic time warping (DTW). For each sentence, the DTW distance constituted a metric of phonetic distance from one speaker to all other speakers. SA comprised the average of these distances. Later, the same participants performed a word recognition task on the target nouns in the same sentences, under three degraded listening conditions. In all three conditions, accuracy increased with SA. This held even when participants listened to their own utterances. These findings suggest that listeners process speech with respect to the statistical properties of the language spoken in their community, rather than using their own speech as a reference.