Miracle-dz/UniFAR-FTUs huggingface dataset:
Degradation and Removal of Paper Mill Dye Basic Orange 2 by Electrochemical Treatment Method

This study deals with the treatment of Paper Mill dye Basic Orange 2, by electrochemical degradation. The graphite carbon electrodes were used as anode and cathode. During electrochemical degradation, the chromoporic groups and aromatic rings were destroyed. The experimental results indicated that initial pH, current density and supporting electrolytes such as NaCl and Na2SO4 were played an important role in degradation of dye. The maximum colour removal efficiency of 85% and total organic compound (TOC) 93% could be achieved for dye, at pH 3, current density 340 A m, NaCl 2.5 g L. The results revealed the suitability of the present process for the effective degradation of dye effluents.
DESIGN RULES

l. Introduction During the already long existence of gasifiers, many quite different designs have been proposed and actually used. In essence the design is a simple one; a broad tubular reactor, a grate on which the fuel lays, one or more entrances for air, and an exit for the gas. Feed is coming in at top, the remaining ash (commonly) goes out at the bottom. The construction may be simple, the actual chemical and technical process is rather complicated, and still not completely understood. To have gasification occurring is easy, to execute it optimally, with high. efficiency and high gas quality is still difficult. The more so because basic principles are not completely clear, and never quantitatively described. Hence the many designs and different approaches. The controlling of the actual gasification process is still more an art than science. It is nevertheless tried to obtain some general rules which can guide the designing process.
Adaptive Learned Bloom Filters under Incremental Workloads

The recently proposed paradigm of learned Bloom filters (LBF) seems to offer significant advantages over traditional Bloom filters in terms of low memory footprint and overall performance as evidenced by empirical evaluations over static data. Its behavior in presence of updates to the set of keys being stored in Bloom filters is not very well understood. At the same time, maintaining the false positive rates (FPR) of traditional Bloom filters in presence of dynamics has been studied and extensions to carefully expand memory footprint of the filters without sacrificing FPR have been proposed. Building on these, we propose two distinct approaches for handling data updates encountered in practical uses of LBF: (i) CA-LBF, where we adjust the learned model (e.g., by retraining) to accommodate the new "unseen" data, resulting in classifier adaptive methods, and (ii) IA-LBF, where we replace the traditional Bloom filter with its adaptive version while keeping the learned model unchanged, leading to an index adaptive method. In this paper, we explore these two approaches in detail under incremental workloads, evaluating them in terms of their adaptability, memory footprint and false positive rates. Our empirical results using a variety of datasets and learned models of varying complexity show that our proposed methods' ability to handle incremental updates is quite robust.
Algorithms for Segmentwise Computation of Forward and Inverse Discrete-time Wavelet Transform

The paper describes a method of segmented wavelet transform (SegWT) that makes it possible to compute the discrete-time wavelet transform of a signal segment-by-segment, with exactly the same result as if the whole signal were transformed at once. Due to its generality, the method can be utilized in many situations: for wavelet-type processing of a signal in real time or in case we want to process the signal in parallel or in case we need to process a long signal, but the available memory capacity is insufficient (e.g. in the DSPs). In the paper, the background theory and the emerging principles of both the forward and the inverse SegWT are explained.
Tibial Stress Fracture is there a Need for Vitamin D Supplementation?

Treating vitamin D deficiency is of key importance in preventing stress fractures. The importance of nutrition in athletes is essential, especially in the female athlete. Both inadequate intake of calcium, vitamin D or inadequate caloric intake, are associated with reduced bone mass. Vitamin D deficiency leads to the pathogenesis of osteoporosis, which further increases the risk of fragility fractures. The article describes numerous risk factors that also increase the risk of tibial stress fractures, with previous stress fracture consistently the strongest predictor of subsequent stress fractures in both sexes. Assessment and management of tibial stress fractures depends on whether the anterior or posterior cortex is involved. Rehabilitation and management options are discussed to help rehabilitate this complex pathology. Ongoing studies are encouraged to improve the knowledge, awareness and attitude towards vitamin D. There is a need for a public health initiative to reduce the risk of stress fractures.
Gender-Role Attitudes and Intrahousehold Time Allocation: Evidence from British Couples∗

We analyze the role of spousal gender-role attitudes on both the wife and the husband’s allocation of time to market work and domestic chores. Using data from the British Household Panel Survey we find that only the women’s time use choices are affected by spousal attitudes. Both partners attitudes have a positive effect on the time a woman allocates to market work, and a negative effect on the time she allocates to domestic work. The influence of the husband’s gender-role attitude on his wife’s time allocation vanishes for couples where the balance of power tilts in her favour.
Poster:Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Machine learning (ML) has progressed rapidly during the past decade. Nowadays, it has become the core component in many industrial domains ranging from automotive manufacturing to financial services. Leading Internet companies, such as Google,1 Amazon,2 and Microsoft,3 further provide Machine Learning as a Service (MLaaS) to simplify ML deployment. In this setting, an MLaaS provider trains a machine learning model at their backend and provides the trained model to public as a black-box API. The major factor that drives the current ML development is the unprecedented large-scale data. In consequence, collecting high-quality data becomes essential for building advanced ML models. Data collection is a continuous process as enormous data is being generated at every second. This turns ML model training into a continuous process as well: Instead of training an ML model for once and keeping on using it afterwards, the model provider, such as an MLaaS provider, needs to keep on updating the model with newly-collected data. In practice, this is also known as online learning. And we refer to the dataset used to perform model update as the updating set. Regularly updating an ML model results in the model having different versions with respect to different model parameters. This indicates that if an ML model is queried with the same set of data samples at two different points in time, it will provide different outputs.
Parallel Cascade Correlation: A GPU Implementation

This paper presents a new implementation of the cascade correlation architecture which leverages the parallel computing capabilities of GPUs. It shows that by combining the inherent parallelization potential of evolutionary algorithms with the caching properties of the cascade correlation architecture, the algorithm can be simplified to a few easily parallelizable operations. It also comes with an open-source CUDA C++ implementation of the resulting genetic cascade correlation algorithm.
Application of lidar ceilometer in Beijing

Vertical variations of aerosols and the effects responded to the emission control: application of lidar ceilometer in Beijing during APEC, 2014 G. Tang, X. Zhu, B. Hu, J. Xin, L. Wang, C. Münkel, G. Mao, and Y. Wang State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Vaisala GmbH, Hamburg 22607, Germany Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
New findings suggest selenium supplementation to reduce the risks of PROM in pregnancy: a new overview concerning supplementation during pregnancy

Diet during pregnancy and lactation is very important as it has a fundamental impact on the health of both the mother and her child. To optimize the woman’s health and to reduce the risk of malformations or illness in the unborn child, it is crucial that the mother reaches an optimal nutritional state before and during gestation and during breastfeeding, when nutritional requirements are still high [5]. Pregnant and lactating women should always consume a varied diet meeting nutritional requirement as defined in national and international dietary guidelines. Pregnant women have an increased energy requirement, especially in the second and third trimesters, of about 300-400 kcal/day. During breastfeeding, the mother requires an extra 500 kcal/day. Increasing the amount of food usually consumed is normally sufficient to achieve the calorie requirement during pregnancy and lactation. Nevertheless, supplements may be needed if some nutrients are lacking. However, the pregnant women should be advised that exceeding recommended doses may be dangerous for her and her unborn child.
Antibiotic resistance profile and adhesion properties of Lactobacillus crispatus M247

In an attempt to better characterize the strain M247, a strain of Lactobacillus crispatus isolated in 1989 from the faeces of a healthy newborn baby, we have further investigated its antibiotic resistance profile, evaluating also antibiotics not recommended by the EFSA, and analyzing in addition its viability in the presence of a high concentration of boric acid, a molecule commonly use to eradicate antimycotic-resistant fungi from the vagina. At the same time, we have evaluated the ability of M247 to adhere to vaginal epithelial cells to get a better understanding of its vaginal colonizing properties. According to the obtained results, M247 appears to be insensitive to metronidazole, sulfamethoxazole and boric acid. Such resistance is not transferrable due to the total absence of mobile elements inside its genome. Moreover, the M247 strain has been shown to adhere by more than 70% to vaginal epithelial cells, thereby providing a mechanistic explanation of its vaginal colonizing capabilities observed both after local and oral use.
OPTH_A_183174 1203..1223

Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: https://youtu.be/XQDu1v60zWY Abstract: Obstructive meibomian gland dysfunction is the most common cause of dry eye. Its conventional treatment has focused on using heat and pressure with anti-inflammatory and antimicrobial therapies but has often been a frequent, frustrating experience for patient and physician. New evidence from Meibomian gland intraductal probing suggests fixed intraductal strictures and obstruction correlating to periductal fibrosis first described in 1997. The use of intraductal probing has been reported, by this author and in at least ten independent peer-reviewed academic papers from around the world, to consistently lead to statistically significant improvement in signs and symptoms of gland dysfunction including cases refractory to other extensive treatments. This review will focus on the background of meibomian gland intraductal probing as well as patient selection, procedure, and perspectives.
DSNRA: a Dynamic Substrate Network Reconfiguration Algorithm

Considering the low acceptance rate and unbalanced load issue in virtual network embedding (VNE), a new reconfiguration algorithm in dynamic substrate network environment, called DSNRA, was proposed. The general process of DSNRA is as follows: a new physical node and related links would be added at first when a virtual request was rejected due to bottleneck substrate devices. Then DSNRA found the bottleneck node and other high load rate physical nodes within the distance constraint and put them in a corresponding set. Next, comparing the revenue with the migration cost of the already mapped virtual nodes which were in the above mentioned set, the decision would be made whether to relocate them and their related virtual links. Finally, adding another new physical node and related links and repeat the before steps continuously until the node load rate of the set is under the pre-set index. Experimental results demonstrated that DSNRA could improve acceptance rate and balance the load.
Safety (cid:54) = Security A security assessment of the resilience against fault injection attacks in ASIL-D certiﬁed microcontrollers

—With the ever increasing amount of electronic components in vehicles and in particular the amount of complex autonomous actions that these components perform, security topics become more and more important in the automotive industry. Not only from a business point of view, as valuable IP assets are contained within these components, but also from a safety point of view, especially when vulnerabilities lead to remotely exploitable attacks. Logical attacks in this ﬁeld are abundant, but attacks using hardware centered techniques such as fault injection are underrepresented. Researchers and professionals often pay no attention in these attacks because they require physical access to the chip, ignoring that the assets obtained (e.g. ﬁrmware, keys, etc.) can be used later to prepare a remote attack. This work aims to address the lack of attention on fault injection attacks by investigating two modern microcontroller units that receive the highest safety assurance rating (ASIL-D) of the ISO 26262 standard. This is done in both a theoretical characterization setup and a more realistic setup where debugging interfaces are targeted. The results obtained from these setups show that the mechanisms implemented to adhere to this maximum safety rating do not adequately protect against fault injection attacks and are therefore insufﬁcient to ensure security by themselves – additional countermeasures are required. Each setup required approximately one week of preparation, but once the attacker ﬁnds the optimal fault injection parameters, the attack can be repeated in less than an hour. We provide some recommendations on what type of countermeasures should be considered to improve the security with respect to fault injection attacks and also provide several pointers to continue the security research in this area.
Influence de la taille, la source et la composition chimique sur les effets sanitaires des particules*

Les particules fines constituent un mélange hétérogène et complexe qui évolue dans le temps et dans l’espace. Elles peuvent avoir des caractéristiques physiques diverses et être constituées d’une multitude de composants chimiques, dont un grand nombre ont été décrits comme potentiellement toxiques. Par conséquent, l’identification et la quantification des influences de composants spécifiques ou de mélanges associés à des sources spécifiques sur des mesures d’effets sur la santé constituent un des domaines les plus complexes de la recherche en santé environnementale, notamment lorsque les particules interagissent avec d’autres co-polluants. Les connaissances actuelles ne permettent pas une quantification précise ou un classement des composés les plus à risque, et les associations observées avec la santé peuvent résulter de composants multiples agissant sur différents mécanismes physiologiques. Certains résultats suggèrent un degré de toxicité différentielle, c’est-à-dire des associations plus fréquentes entre les particules liées au trafic automobile, les particules fines et ultrafines, les métaux particulaires et le carbone élémentaire, et différents effets sanitaires graves, comme une mortalité et une morbidité accrues liées à des affections cardiovasculaires et respiratoires. Un programme soigneusement ciblé de recherche toxicologique et épidémiologique, intégrant des approches plus sophistiquées (comme le recueil de davantage de données de spéciation, des techniques de modélisation plus fines, une évaluation d’exposition précise et une meilleure définition de la sensibilité individuelle) et une collaboration optimale entre des équipes pluridisciplinaires, est aujourd’hui nécessaire. Cela facilitera la mise en œuvre de politiques de gestion ciblées, de mesures de contrôle de la pollution plus efficaces et, au final, la réduction des maladies imputables à la pollution particulaire. The influence of size, source and chemical composition on the health effects of particulate matter
Analysis and Design of a Resonant Power Converter With a Wide Input Voltage Range for AC/DC Applications

Resonant converter topologies have the ability to eliminate switching losses through zero-voltage switching (ZVS), making them well-suited for switching operation in the MHz frequency range. However, these types of converters are traditionally very sensitive to changes in input voltage and power level, making them unsuitable as power factor correcting ac–dc converters. This article presents a thorough analysis of the operation of a class DE converter in order to derive a set of conditions under which it can achieve a constant input impedance over a wide input-voltage range (60–325 Vdc) with a constant output voltage (450 Vdc) and, thus, be operated as a power factor correctional (PFC) converter, while maintaining ZVS across the full range. The operation is experimentally verified under dc–dc operation for different power levels at a series of input voltages within the specified range. The implemented prototype achieves conversion efficiencies of up to 94% and handles up to 105-W power at a switching frequency of 2 MHz and above, while achieving constant input impedance over the full input-voltage range, enabling its use as a power factor correcting converter.
Structures related to parallelizations

A classical theorem of Kervaire states that products of spheres are parallelizable if and only if at least one of the factors has odd dimension. The versor field given by the complex multiplication on the odd-dimensional factor gives explicit parallelizations B (if the odd dimensions is n = 1, 3), P (for any odd n). In this paper we use B, P to obtain orthogonal and symmetric orbits of B, P-invariant G-structures on Sm×Sn, where G = U((m+n)/2), Sp((m+n)/4), G2, Spin(7), Spin(9). This approach leads to an alternative description of classical structures as well as to new structures on products of spheres.
Real Time Technical Expert ’ s Recommendation from Social Media

Now a day’s social network plays a synergistic role in solving user queries. A platform such as ask.fm solves user queries in real time but the computation time as well as waiting time is undetermined. We propose new platform to solve the challenging problems related to various social networking sites by effectively connecting user queries to social streams by recommending experts profile . We propose our User-Expert connection web interface through which user can ask query. User’s query get analyzed by LDA algorithm to identify topic of the query.L2R algorithm is use to rank expert’s profile and recommended to user. User can interact with expert in real time.
TESTING OF MITOCHONDRIAL DNA AND MITOCHONDRIAL RESPIRATORY CHAIN COMPLEXES (REFERENCE – 2014.02.09)

services sociaux (INESSS), and that version can be consulted at http://www.inesss. qc.ca/fileadmin/doc/INESSS/Analyse_biomedicale/Decembre_2014/INESSS_Avis_Ministre_anal yses_bio_med_dec_2014_9. pdf. It was translated into English by the Canadian Agency for Drugs and Technologies in Health (CADTH) with INESSS’s permission. INESSS assumes no responsibility with regard to the quality or accuracy of the translation.
A Survey on Classification in R Programming Using Data Mining

A long standing problem in Data mining is that data to be found unstructured due to its different format. Classification is a crucial analysis topic in data mining. At a present time, data mining sector presents a brand new data model. The activation values of the hidden units within the network are analyzed and classification rules are generated exploitation the results of this analysis. R Programming consists of diverse ready-to-use applied statistical modeling algorithms that permits user to form duplicable analysis and develop information product. R Studio is a good interface for R Programming is employed extensively for generating reports supported many current trends models like random forest, support vector machine,C4.5, k-means, Apriori, EM, Page, Rank, AdaBoost, KNN, NaïveBayes, CART. R extremely stands unique for massive quantity of inherent statistical formulae and algorithms. This present survey focuses on existing classification algorithms using data mining techniques, Comparison table, Applications that is widely used in R programming.
Time Is Not of the Essence Understanding the Neural Correlates of Mental Time Travel

The precise relationship between memory and imagination has been a matter of debate for centuries (e.g., Aristotle [Barnes, 1984]; Hobbes, 1668; Hume, 1739; Russell, 1921). But at no time has this debate seen a more remarkable development than in the last two decades, as numerous behavioral, neuropsychological, and neuroimaging studies have consistently shown that our capacity to remember episodes that occurred in the past and our ability to imagine possible events that may occur in the future are profoundly intertwined. Although somewhat cautiously, many of these results have been interpreted as providing evidence in favor of the view that episodic memory (i.e., our capacity to remember past personal events) and episodic future thinking (i.e., our ability to imagine possible future personal events) (Atance & O’Neill, 2001; Szpunar, 2010) should be seen as two operations of a single cognitive system that enables “mental time travel” (MTT) (Tulving, 1985): the allegedly uniquely human psychological capacity through which we “mentally project ourselves in time” by entertaining mental simulations of events whose contents are either about the past or about the future (Suddendorf & Corballis, 1997). According to this perspective, then, the psychological system for MTT operates upon representations with temporal or “tensed” contents. This view naturally leads to the hypothesis that it is the temporal nature of the content of the mental simulations involved in the aforementioned results that accounts for the common engagement of neural mechanisms during episodic memory and future thinking. Time, as it were, is thought to be of the essence.1 However, as we review in the present chapter, the story is not so simple. As it turns out, there is a large amount of evidence that is difficult to reconcile with the view that what accounts for the common engagement of the neural substrates of episodic memory and future thinking is the fact that the contents of such mental simulations are tensed. Indeed, we argue that much of this evidence actually suggests, somewhat paradoxically, that when it comes to the neural mechanisms of mental time travel, time is not of the essence— or, at least, not essential as part of the OUP UNCORRECTED PROOF – FIRSTPROOFS, Mon Feb 01 2016, NEWGEN