Nano-Ticl4.SiO2: a Versatile and Efficient Catalyst for Synthesis of Dihydropyrimidones via Biginelli Condensation

Nano-TiCl4.SiO2 has been found to be an extremely efficient catalyst for the preparation of 3,4-dihydropyrimidinones/thiones via three-component reactions of an aldehyde, β-ketoester or β-diketone and urea or thiourea under mild conditions. Nano-TiCl4.SiO2 as a solid Lewis acid has been synthesized by reaction of nano-SiO2 and TiCl4. The structural characterization of this acid has been studied by FT-IR (ATR), XRD, SEM and TEM. This process was simple and environmentally benign with high to excellent yields. Furthermore, the catalyst could be recovered conveniently and reused for at least three times.KEYWORDS: Nano-TiCl4.SiO2, heterogeneous catalyst, multi-component reaction, β-ketoester, β-diketone, urea

Neural Ranking Models with Weak Supervision

Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection(Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models.Comment: In proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2017

Stability of glassy hierarchical networks

The structure of interactions in most animal and human societies can be best represented by complex hierarchical networks. In order to maintain close-to-optimal function both stability and adaptability are necessary. Here we investigate the stability of hierarchical networks that emerge from the simulations of an organization type with an efficiency function reminiscent of the Hamiltonian of spin glasses. Using this quantitative approach we find a number of expected (from everyday observations) and highly non-trivial results for the obtained locally optimal networks, including, for example: (i) stability increases with growing efficiency and level of hierarchy; (ii) the same perturbation results in a larger change for more efficient states; (iii) networks with a lower level of hierarchy become more efficient after perturbation; (iv) due to the huge number of possible optimal states only a small fraction of them exhibit resilience and, finally, (v) 'attacks' targeting the nodes selectively (regarding their position in the hierarchy) can result in paradoxical outcomes

Identifying Retweetable Tweets with a Personalized Global Classifier

In this paper we present a method to identify tweets that a user may find interesting enough to retweet. The method is based on a global, but personalized classifier, which is trained on data from several users, represented in terms of user-specific features. Thus, the method is trained on a sufficient volume of data, while also being able to make personalized decisions, i.e., the same post received by two different users may lead to different classification decisions. Experimenting with a collection of approx.\ 130K tweets received by 122 journalists, we train a logistic regression classifier, using a wide variety of features: the content of each tweet, its novelty, its text similarity to tweets previously posted or retweeted by the recipient or sender of the tweet, the network influence of the author and sender, and their past interactions. Our system obtains F1 approx. 0.9 using only 10 features and 5K training instances.Comment: This is a long paper version of the extended abstract titled "A Personalized Global Filter To Predict Retweets", of the same authors, which was published in the 25th ACM UMAP conference in Bratislava, Slovakia, in July 201

Synthesis and Characterization of 5-Substituted 1H-Tetrazoles in the Presence of Nano-TiCl4.SiO2

Nano-TiCl4.SiO2 was found to be an extremely efficient catalyst for the preparation of 5-substituted 1H-tetrazole derivatives. Nano-TiCl4.SiO2 is a solid Lewis-acid was synthesized by the reaction of nano-SiO2 and TiCl4. The structure characterization of this acid was achieved with X-ray diffraction, thermogravimetric analysis and electron microscopy. The synthesis of the catalyst is simple and environmentally benign with a good yield. Furthermore, the catalyst is conveniently recoverable and was reused for at least three times. The antimicrobial activities of the synthetic compounds were also determined by both micro dilution methods as recommended by the Clinical Laboratory Standard Institute, but unfortunately did not exhibit antibacterial activities at the highest concentration (256 μL mL–1). Further studies are still needed to investigate the potential biological activities of these compounds against other diseases.KEYWORDS Nano-TiCl4.SiO2, heterogeneous catalyst, 5-substituted 1H-tetrazoles, antibacterial.PDF and Supp files attache

DEVELOPMENT OF A MICROFLUIDIC GAS GENERATOR FROM AN EFFICIENT FILM-BASED MICROFABRICATION METHOD

Recently, tape&film based microfabrication method has been studied for rapid prototyping of microfluidic devices due to its low cost and ease of fabrication [1]. But most of the reported film-based microfluidic devices are simple single-layer patterned 2D designs, whose potential applications are limited. In this paper, we present the design, fabrication and testing results of a 3D structured microfluidic gas generator prototype. This gas generator is used as an example to introduce our new approach of film-based fabrication method towards lab-use microfluidic research, which usually requires constant change of design and prefers low fabrication cost and short fabrication period. The prototype is a film-based comprehensive microfluidic gas generator which integrates self-circulation, self-regulation, catalytic reaction and gas/liquid separation. Time and economy efficiency are the biggest merit of this method. The only required facility during the whole process is a digital craft-cutter. The working principle of the device is illustrated in Fig.1

Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming

Pembalut Wanita Ramah Lingkungan dan Beretika

Sanitary napkins during menstruation is a primary requirement. According to the research, disposable sanitary napkin that is now being used contain hazardous substances that could potentially cause disease harmful to the reproductive organs. In addition, there is no special handling for waste disposable sanitary napkins. Although there are safe cloth napkin products, but they are expensive. This program aims to create innovative sanitary napkins are economical, safe, does not cause interferencee for environment and aesthetics. This Innovation Sanitary Napkins made of old cloth. The method used are designing, manufacture and testing. The results are two kind of design (long and wallet), handbags, socialization, banners, and brochures