Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanotechnology in the Environment—The Good, the Bad, and the Ugly

Nanotechnology is the ability to work at the molecular level, atom by atom, to create larger structures with fundamentally new molecular organization, novel properties, and functions. Engineered nanomaterials, typically a tenth the size of a human cell, are currently being used for a broad range of novel applications, including drug delivery, tissue engineering, tumor treatment, imaging, catalysis, detectors/sensors, and energy storage and transmission devices. Although some nanomaterials have been synthesized since the 1980s, their widespread production is relatively recent and their market size is expected to reach $1 trillion within 10 to 15 years. Such rapid growth suggests the potential for large environmental footprints, some of which will be good, some bad, and some ugly. On the good side, some nanomaterials hold great promise for reducing waste production, cleaning up industrial contamination, providing potable water, and improving the efficacy of energy production and use. The high potential to improve environmental technologies some of which date back to the Victorian era is intrinsically related to the small size of engineered nanomaterials, which results in significantly different properties than the associated bulk materials. Small size translates into a large surface to volume ratio, which implies greater opportunity to interact with environmental pollutants. In a sense, nanomaterials are “all surface. ” This can be a highly desirable property for water, wastewater, and hazardous waste treatment. Some nanomaterials can be superior adsorbents or catalysts that remove pollutants more efficiently and at a substantially lower cost than current materialintensive approaches such as ion exchange resins and activated carbon adsorption. Nanotechnology also offers the potential for multifunctional materials, such as nano-architectured membranes for water treatment that incorporate chemically reactive nanomaterials to accomplish both separation and degradation of pollutants and enhance antifouling properties. The good news is that many of our colleagues are making significant progress toward the development of environmental nanotechnologies. These include nanosized iron for reductive treatment of chlorinated solvent DNAPLs, nanomagnetite for the removal of arsenic by sorption and magnetic separation, high-performance nanoscale Pd/ Au catalysts for treating particularly challenging contaminants in water that must be removed to a very low level, and novel advanced oxidation and disinfection approaches, to name a few. We hope to publish more papers in these emerging areas of research in the near future. On the bad side, the environment will be increasingly prone to
Unsupervised Learning of a Dictionary of Neural Impulse Responses from Spiking Data ∗

Summary: The majority of approaches for studying neuronal activity use an experimental setup where a stimulus is repeatedly applied over a series of trials with time-locked and non-overlapping events. Then, the spike trains are averaged over trials and smoothed out. These approaches fail in naturalistic environments and experiments in which the stimulus comprises discrete events occurring at random times, which may elicit overlapping responses. To analyze neuronal activity patterns in such experiments, we utilize a model of the spiking rate of a neuron as the convolution of an unknown impulse response and a sparse code, representing the time when the stimulus elicits an activity pattern in the neurons response, and the response’s amplitude. We ﬁt the model to single-unit spiking data by solving a Poisson dictionary learning problem that lets us estimate a neurons impulse response, and the amplitude of the response to each stimulus, directly from the spiking data. To solve the problem, we construct an autoencoder. We used neural spiking data acquired from piriform cortex in response to odor pulses to estimate the impulse responses (dictionary) of ∼ 200 neurons along with the strength of the response associated with each pulse for each neuron (sparse code). The Kolmogorov-Smirnov (KS) test shows that the model ﬁts the data well. Our analysis shows that, at the level of a single neuron, the odor pulses evoke diﬀerent responses, likely reﬂecting diﬀerences in alignments to the breathing phase. In addition, we found that neurons from the population cluster according to either the estimated impulse responses or the stimulus responses, suggesting the presence of distinct neural populations in piriform cortex that could have distinct roles in the processing of information in olfactory search. Experimental setup. An
CONTENT IN BIOACTIVE COMPOUNDS AND ANTIOXIDANT CAPACITY OF FLOURS OBTAINED FROM WINEMAKING BY- PRODUCTS

Grape pomace is particularly noted for its content of fiber, phenolic compounds, unsaturated lipids and sterols, vitamins and antioxidants. In this paper are presented results of the performed research for determination of vitamin C, total polyphenol content and antioxidant capacity in case of flours (grape pomace flour, grape seed flour, grape skin flour) obtained from winemaking by-products. There were taken into the study, winemaking by-products from three grape varieties: Blauer Zweigelt, Burgund Mare and Fetească Regală. Vitamin C content of flours obtained from winemaking by-products varied in the range of 18.25-25.75 mg/100 g FW and total polyphenol content varied in the range of 57.45-258.75 mg gallic acid equivalent (GAE)/g FW. Due to the content in bioactive compounds, the obtained flours have an antioxidant capacity (9.98-46.12 mg trolox equivalent (TE)/g FW). The highest value of the antioxidant capacity was recorded in the case of grape seed flour, this one being followed by the grape pomace flour, on the last place being the grape peel flour. Due to the complex biochemical composition, the flours obtained from winemaking byproducts, can be considered as functional ingredients.
Evaluation of Uric Acid, Creatinine, and Plasma Ions in Rats Fed with High-fat Diet Mitigated by Wild, Edible Beans

Aim and Objectives: It is a known fact that, consumption of fruits and vegetables rich in potassium and other ions is a critical component of the special diet approach to controlling hypertension and decrease cardiovascular disease. Less well documented are the effects of under-utilized wild beans which may help reduce the risk of metabolic diseases instigated by high fats diet. Hence this present study is to compare the biochemical effectiveness of some selected wild and edible beans on rats fed with high-fat diets. Results and Conclusion: Lipid profile result carried out in the blood showed high density lipoprotein to be significantly (P ≤ 0,05) high in Pakala group with a value of 2.2±0.02 compare to other groups. While for low density lipoprotein (LDL); rats with Otili in their diet had the highest LDL with a value of 0.45±0.01. However, the group of rats fed with Feregede had the least cholesterol level compared to other groups of rats fed with respective wild beans and the negative control group. There, was a significant difference in the phosphorus level between the wild and edible beans and pronounced impact of High-fat diet in rats fed with Feregede compared to other rats fed with other wild beans.
Magnetic-field induced formation of 1D Fe3O4/C/CdS coaxial nanochains as highly efficient and reusable photocatalysts for water treatment

We report a novel strategy for fabrication of one-dimensional (1D) Fe3O4/C/CdS coaxial nanochains via a magnetic field-induced assembly and microwave-assisted deposition method. First, 1D pearl chain-like Fe3O4/C core–shell nanocables are successfully assembled via the hydrothermal reaction of Fe3O4 nanospheres and glucose in water in the presence of an external magnetic field. The carbonaceous layer is about 10 nm in thickness, and it acts as the stabilizer for the Fe3O4 nanochains. Afterwards, CdS nanoparticles are facilely deposited onto the 1D Fe3O4/C nanochains via a rapid microwave-irradiation route to form 1D Fe3O4/C/CdS coaxial nanochains. Further investigation has revealed that these magnetic nanocomposites possess significantly improved activity as a recyclable photocatalyst for the degradation of organic pollutants when exposed to visible light irradiation. This new synthesis strategy is not restricted to the specific material discussed in this work and should be versatile for a wide range of magnetically separable photocatalysts containing 1D magnetic nanochains as the support and an outer layer of active semiconductor nanocrystals.
Reaktionen mit Carbenen und Iminen als Zwischenstufen Zur photochemischen und thermischen Umwandlung organischer Stickstoff‐Verbindungen

Die Stickstoff-Abspaltung aus organischen Molekulen kann zu reaktionsfahigen, ungeladenen Zwischenstufen mit Elektronensextett fuhren, zu Derivaten des Methylens ∣CH2 oder des Imins ∣NH. Andere Bildungsweisen dieser Zwischenstufen werden zum Vergleich herangezogen. Ihre Reaktionen in Abhangigkeit von Struktur und Entstehung werden besprochen, wobei praparativ verwertbare Umsetzungen im Vordergrund stehen.
A Comparative Case Study of Positive and Negative Effects of Localization in Asian Subsidiaries of Japanese MNEs

Based on the findings in author’s interviews to Asian subsidiaries of Japanese MNEs, this study focuses on an interesting contrast of “knowledge appropriation vs. knowledge sharing”. Using “G vs. W”-model framework on the misalliance problem in gray areas management, the three interviewed cases are compared where the relatively detailed information was available on the decision making by the promoted HCNs, so that the arising mechanism for the opposing results of either “knowledge appropriation vs. knowledge sharing” is examined. Looking into the observed differences among these three cases, “the two key explanatory factors” are pointed out as “a pair of different set of conditions” which can lead to the opposing results of “knowledge appropriation vs. knowledge sharing”; i.e., (i) “degree of dependence on personal skills and knowledge of core members” is likely to raise incentives for the promoted HCNs for “knowledge appropriation” and (ii) “relative size of prospects for growing opportunities” is likely to raise incentives for them for “knowledge sharing”. Then, several case specific factors (e.g., market condition, business characteristics, communication skill of PCNs, etc.) are pointed out as underlying reasons for relative sizes of these key explanatory factors. Furthermore, looking into a dynamic feedback of the first key explanatory factor, a pair of contrasting mechanisms of “vicious cycles” (in case of “knowledge appropriation”) vs. “virtuous cycles” (in case of “knowledge sharing”) is discussed.
Redefining Value Creation

3 Redefining Value Creation . 3 Introducing Integrated Value . 4 Five Forces of Fragmentation . 5 Five Forces of Integration . 6 Five Pathways for Innovation . 7 Differentiating Integrated Value.9 Illustrating Integrated Value . 10 Implementing Integrated Value . 11 Conclusion . 13 References . 14 Declarations . 17 Contact . 17 Citation . 17
Combined U-corrected Pb-Pb dating and Al-26-Mg-26 systematics of individual chondrules - Evidence for a reduced initial abundance of Al-26 amongst inner Solar System chondrules

Chondrites are fragments of asteroids that avoided melting and, thus, provide a record of the material that accreted to form protoplanets. The dominant constituent of chondrites are millimeter-sized chondrules formed by transient heating events in the protoplanetary disk. Some chondritic components, including chondrules, contain evidence of the extinct short-lived radionuclide 26 Al (half-life of 0.73 Myr). The decay of 26 Al is postulated to have been an important heat source promoting asteroidal melting and diﬀerentiation. Thus, understanding the 26 Al inventory in the accretion regions of diﬀerentiated asteroids is critical to constrain the accretion timescales of protoplanets. The current paradigm asserts that the canonical 26 Al/ 27 Al ratio of (cid:1) 5 (cid:3) 10 (cid:4) 5 recorded by the oldest dated solids, calcium-aluminium refractory inclusions, represents that of the bulk Solar System. We report, for the ﬁrst time, the 26 Al- 26 Mg systematics of chondrules from the North West Africa (NWA) 5697 L 3.10 ordinary chondrite and Allende ratio of (1.36 ± 0.72) (cid:3) 10 (cid:4) 5 deﬁned by the eight chondrules, thermal modelling constrains the accretion of diﬀerentiated planetesimals formed with this 26 Al inventory from (cid:1) 0.1 to (cid:1) 0.9 Myr after Solar System formation to ensure melting by 26 Al decay. (cid:1) 2019
Scientiﬁc usage of the Pencil Code Search results using and Bumblebee

Figure 1: Number of papers since 2003 that make use of the Pencil Code. In red is shown the number of papers that reference it for code comparison or other purposes and in blue the papers that are not co-authored by Brandenburg. The enhanced number of papers during 2011–2013 results from publications related to his ERC Advanced Grant. A search using ADS https: //ui.adsabs. harvard.edu/ lists the papers in which the Pencil Code is being quoted. In the following we present the papers that are making use of the code either for their own scientific work of those authors, or for code comparison purposes. We include conference proceedings, which make up 15–20% of all papers. We classify the references by year and by topic, although the topics are often overlapping. The primary application of the Pencil Code lies in astrophysics, in which case we classify the papers mostly by the field of research. Additional applications can also be found in meteorology and combustion.
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory

Bayesian Neural Networks (BNNs) are a promising approach towards improved uncertainty quantiﬁcation and sample efﬁciency. Due to their complex parameter space, choosing informative priors for BNNs is challenging. Thus, often a naive, zero-centered Gaussian is used, resulting both in bad generalization and poor uncertainty estimates when training data is scarce. In contrast, meta-learning aims to extract such prior knowledge from a set of related learning tasks. We propose a principled and scalable algorithm for meta-learning BNN priors based on PAC-Bayesian bounds. Whereas previous approaches require optimizing the prior and multiple variational posteriors in an interdependent manner, our method does not rely on difﬁcult nested optimization problems. Our experiments show that the proposed method is not only computationally more efﬁcient but also yields better predictions and uncertainty estimates when compared to previous meta-learning methods and BNNs with standard priors.
CONVERSATION OF REAL IMAGES INTO CARTOONIZE IMAGE FORMAT USING GENERATIVE ADVERSARIAL NETWORK

Our aim is to creating animated images (Cartoon images) with the help of real images in high resolution that considered very challenging and valuable in today’s computer vision and computer graphics area. The Traditional technique was quite time taking and it require an artistic mindset, but sometime it do not produce satisfactory results. Then we analysis the behavior of cartoon paintings after then, we propose three separately identify sections. (1) The surface representation (2) The structure representation (3) The texture representation. Where our model will first decompose the real image and then start learning from that decompose image. For these we are using Generative Adversarial Network (GAN) framework. Cartoon GAN will be used for the learning real–world image and extract them into Cartoonized image.
APPLICABLE CROWDSENSING TECHNIQUES FOR SMART CITIES: A BIBLIOMETRIC REVIEW

: The United Nations project that 68% of the world population will be living in urban areas by the year 2050. Due to the growing importance of urban living, making cities a better place to live has become a more relevant theme. However, not all countries have the economical conditions to implement costly hardware, therefore, crowdsensing becomes a feasible alternative. The aim of this study is to perform a bibliometric review of crowdsensing techniques that could be applicable to Smart Cities.
Sentiment Analysis in Tunisian Social Media Using Deep Learning

Tunisian dialect, is different from Modern Standard Arabic (MSA). In (Fourati et al., 2020), TUNIZI was referred to the Romanized alphabet used to transcribe informal Arabic for communication by the Tunisian Social Media community. (Younes et al., 2015) mentioned that 81% of the Tunisian comments on Facebook used Romanized alphabet. In (Abidi, 2019), a study was conducted on 1,2M social media Tunisian comments (16M words and 1M unique word) showed that 53% of the comments used Romanized alphabet while 34% used Arabic alphabet and 13% used code-switching script. The study mentioned also that 87% of the comments based Romanized alphabet are TUNIZI while the rest are French and English.
Self-assembled biomimetic catalysts: studies of the catalase and peroxidase activities of Mn(III)-Schiff base complexes

Five Mn(III) nitrate complexes have been synthesized from dianionic hexadentate Schiff bases obtained by the condensation of 3-ethoxy-2-hydroxybenzaldehyde with different diamines. The complexes have been characterized by elemental analysis, ESI mass spectrometry, IR and 1H NMR spectroscopy, r. t. magnetic, and molar conductivity measurements. Parallel-mode EPR spectroscopy of 1 is also reported. Ligand H2L3 and complexes [MnL1(H2O)2](NO3)(CH3OH) (1), [MnL3(H2O)2]2(NO3)2(CH3OH)(H2O) (3), and [MnL4(H2O)2](NO3)(H2O)2 (4) were crystallographically characterized. The X-ray structures show the self-assembly of the Mn(III)–Schiff base complexes through µ-aquo bridges between neighboring axial water molecules and also by π–π stacking interactions, establishing dimeric and polymeric structures. The peroxidase and catalase activities of the complexes have been studied. Complexes with the shorter spacer between the imine groups (1–2) behave as better peroxidase and catalase mimics, probably due to their ability to coordinate the hydrogen peroxide substrate to manganese.
University of Birmingham Functional connectivity alterations in epilepsy from resting-state functional MRI

The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.
Investigation of Cu(In,Ga)Se2 thin‐film formation during the multi‐stage co‐evaporation process

In order to transfer the potential for the high efficiencies seen for Cu(In,Ga)Se2 (CIGSe) thin films from co‐evaporation processes to cheaper large‐scale deposition techniques, a more intricate understanding of the CIGSe growth process for high‐quality material is required. Hence, the growth mechanism for chalcopyrite‐type thin films when varying the Cu content during a multi‐stage deposition process is studied. Break‐off experiments help to understand the intermediate growth stages of the thin‐film formation. The film structure and morphology are studied by X‐ray diffraction and scanning electron microscopy. The different phases at the film surface are identified by Raman spectroscopy. Depth‐resolved compositional analysis is carried out via glow discharge optical emission spectrometry. The experimental results imply an affinity of Na for material phases with a Cu‐poor composition, affirming a possible interaction of sodium with Cu vacancies mainly via In(Ga)Cu antisite defects. An efficiency of 12.7% for vacancy compound‐based devices is obtained. Copyright © 2011 John Wiley & Sons, Ltd.
Analysis and design of a forward-flyback converter employing two transformers

This paper presents the steady-state analysis and design of a forward-flyback converter that employs two transformers and an output inductor. By utilizing two separate transformers, the proposed converter allows a low-profile design to be readily implemented while retaining the merits of a conventional single-transformer forward-flyback converter with secondary center tap. By using an output inductor, the proposed converter efficiently reduces the output ripple to an acceptable level. The design and performance of the proposed converter are confirmed with experiments on a 100 W prototype converter.
Modeling COVID-19 Nonpharmaceutical Interventions: Exploring periodic NPI strategies

In April 2020, we developed a COVID-19 transmission model used as part of RAND's web-based COVID-19 decision support tool that compares the effects of different nonpharmaceutical public health interventions (NPIs) on health and economic outcomes. An interdisciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health/economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). We previously published a description of our approach as a RAND report describing how the epidemiological model, the economic model, and a systematic assessment of NPIs informed the web-tool. This paper provides further details of our model, describes extensions that we made to our model since April, presents sensitivity analyses, and analyzes periodic NPIs. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies. We consider strategies that periodically switch between a base NPI level and a higher NPI level as our working example.