Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of residential wood combustion on the concentrations of PM2.5 in four Nordic cities

Abstract. Residential wood combustion (RWC) is an important contributor to air quality in numerous regions worldwide. This study is the first extensive evaluation of the influence of RWC on ambient air quality in several Nordic cities. We have analysed the emissions and concentrations of PM2.5 in cities within four Nordic countries: in the metropolitan areas of Copenhagen, Oslo, and Helsinki and in the city of Umeå. We have evaluated the emissions for the relevant urban source categories and modelled atmospheric dispersion on regional and urban scales. The emission inventories for RWC were based on local surveys, the amount of wood combusted, combustion technologies and other relevant factors. The accuracy of the predicted concentrations was evaluated based on urban concentration measurements. The predicted annual average concentrations ranged spatially from 4 to 7 µg m−3 (2011), from 6 to 10 µg m−3 (2013), from 4 to more than 13 µg m−3 (2013) and from 9 to more than 13 µg m−3 (2014), in Umeå, Helsinki, Oslo and Copenhagen, respectively. The higher concentrations in Copenhagen were mainly caused by the relatively high regionally and continentally transported background contributions. The annual average fractions of PM2.5 concentrations attributed to RWC within the considered urban regions ranged spatially from 0 % to 15 %, from 0 % to 20 %, from 8 % to 22 % and from 0 % to 60 % in Helsinki, Copenhagen, Umeå and Oslo, respectively. In particular, the contributions of RWC in central Oslo were larger than 40 % as annual averages. In Oslo, wood combustion was used mainly for the heating of larger blocks of flats. In contrast, in Helsinki, RWC was solely used in smaller detached houses. In Copenhagen and Helsinki, the highest fractions occurred outside the city centre in the suburban areas. In Umeå, the highest fractions occurred both in the city centre and its surroundings.
A Biomimetic Soft Lens Controlled by Electrooculographic Signal

Thanks to many unique features, soft robots or soft machines have been recently explored intensively to work collaboratively with human beings. Most of the previously developed soft robots are either controlled manually or by prewritten programs. In the current work, a novel human–machine interface is developed to use electrooculographic signals generated by eye movements to control the motions and the change of focal length of a biomimetic soft lens. The motion and deformation of the soft lens are achieved by the actuation of different areas of dielectric elastomer films, mimicking the working mechanisms of the eyes of human and most mammals. The system developed in the current study has the potential to be used in visual prostheses, adjustable glasses, and remotely operated robotics in the future.
A Low Noise ASIC for Butterfly Accelerometer

An open-loop switch-capacitor detection circuit with high resolution and low noise is proposed for a butterfly accelerometer. This paper introduced the composition and realization principle of the system, proposed a C/V detection structure and a filter with high Q value. The preamplifier adopted the folded cascode structure with the gain enhancement technology to achieve high GBW and improved the phase margin. The input common mode feedback helps to achieve a high linearity detection and have an ideal dynamic detection range. All of the voltage and current are supplied by the reference source with low temperature coefficient. Under the Cadence Design System, the spectre circuit simulation is performed based on the 0.6um CMOS standard process model. The results show the detection range of the Accelerometer is±0.1pF, sensitivity is 25V/pF, output noise is within 0.4uV/Hz1/2. Keywords-butterfly accelerometer; switch-capacitor detection; gain-enhancement; common mode feedback
EFFECT OF ANTIOXIDANTS AND POTASSIUM ON PRODUCTIVITY OF SUNFLOWER IN SANDY SOIL

uring two summer seasons of 2015 and 2016, the influence of antioxidants application and potassium levels on productivity of sunflower was estimated under sandy soil conditions. Antioxidants (nil antioxidants; tap water only; vitamin E, 50 ppm; vitamin C, 100 ppm; citric acid, 500 ppm and vitamin E + vitamin C + citric acid at the same levels used individually for each). Potassium levels included 0, 24, 36 and 48 kg K2O/feddan (0.42 hectar). Results showed that both antioxidants application and potassium levels treatments had remarkable effects on plant height, head diameter, number of seeds/head, 1000 seed weight, seed and oil yields as well as percentages of harvest index, shelling, seed protein and seed oil in both seasons. Maximum values of such tested traits were commonly recorded with using vitamin E + vitamin C + citric acid. Increasing potassium level from 0 to 48 kg K2O/feddan caused significant increases in all traits of sunflower plants and the highest values were obtained at 48 kg K2O/feddan in both seasons. All studied traits were significantly affected by the interaction between antioxidants application and potassium levels, except harvest index and seed protein percentage.
Simple Synchronous and Asynchronous Algorithms for Distributed Minimax Optimization

: Synchronous and asynchronous algorithms are presented for distributed minimax optimization. The objective here is to realize the minimization of the maximum of component functions over the standard multi-agent network, where each node of the network knows its own function and it exchanges its decision variable with its neighbors. In fact, the proposed algorithms are standard consensus and gossip based subgradient methods, while the original minimax optimization is recast as minimization of the sum of component functions by using a p -norm approximation. A scalable step size depending on the approximation ratio p is also presented in order to avoid slow convergence. Numerical examples illustrate that the algorithms with this step size work well even in the high approximation ratios.
Experiments in neural question answering

In this thesis, we apply deep learning methods to tackle the tasks of finding duplicate questions, learning to rank the answers of a Multiple Choice Question (MCQ) and classifying the answers to a question coming from the context of a paragraph. We draw our attention toward the problems related to sentence-sentence similarity. We used siamese architecture for better representation of the question and answers. The basic aim of all the methods proposed in this thesis is to build the word embeddings of question and answers and feed them to a deep neural architecture. We used several such architecture like Long-Short term memory (LSTM) and Convolutional Neural Network (CNN). We have also implemented an attention mechanism to put more focus on the sentence-word relationship. Our goal was to extract a refined representation of the question and answers through different combination of these deep learning techniques. We generated a representation of a sentence according to the context of another sentence for solving our tasks. We provide some simple but efficient deep learning models to solve our tasks. As neural models are data driven, we train our model extensively by making pairs such as question-question and question-answer over a large-scale real-life dataset. We used three different datasets to solve our three different tasks. The Quora dataset of several question answer pairs was used for the task of finding duplicate question. The OpenTriviaQA question answering dataset for the ranking of multiple answers. Lastly, we use the SQuAD dataset for the answer classification of reading comprehension task. We evaluate our models based on metrics like accuracy, precision, recall, F1 scores. Our methods and experiments demonstrate some significant improvements over the state-of-the-art methods.
Design of Pv System With Multilevel Structures for Generation of Higher Number of Voltage Levels

A new method of generating higher number of voltage levels by stacking multilevel converters of lower space vector structures and having a single DC power supply as mentioned above with low voltage devices is presented in this paper. A photovoltaic system, also PV system or solar power system, is a power system designed to supply usable solar power by means of photovoltaics An important feature of this stacked structure is the use of low voltage devices while attaining higher number of levels. This will find extensive applications in electric vehicles since direct battery drive is possible. Stacked multi-cell converter was introduced to generate more levels. They use less number of capacitors but require more switches. Solar PV has specific advantages as an energy source: its operation generates no pollution and no greenhouse gas emissions once installed, The voltages of all the capacitors in the structure can be controlled within a switching cycle using the switching state redundancies (pole voltage redundancies). This helps in reducing the capacitor size. Also, the capacitor voltages can be balanced irrespective of modulation index and load power factor. A 9-level inverter is developed by stacking two 5-level inverters and an induction motor is run using V/f control scheme. By using the simulation results we can analyze the proposed method at both the steady state and transient state.
Fluorescence In Situ Imaging of Dendritic RNAs at Single‐Molecule Resolution

RNA localization is an important step in gene regulation. Imaging RNAs in fixed and live cells provides contextual information about RNA distribution in the cells. Here, we provide detailed protocols for performing single‐molecule fluorescence in situ hybridization (smFISH). smFISH detects mRNA molecules at single‐molecule resolution in fixed neuronal cells using ∼50 small oligonucleotide probes for each mRNA. The technique has been successfully applied to understand RNA localization and distribution in various biological systems, ranging from Drosophila to the mammalian nervous system. The probes are small enough to bind to structured RNAs or RNAs that are part of RNA‐protein complexes, thereby accounting for ∼85% of the total RNA; this enables a level of sensitivity equivalent to that of quantitative real‐time PCR, but with anatomical resolution. © 2019 by John Wiley & Sons, Inc.
Cycloartane and Stigmastane Type Triterpenoids from Pothos scandens Inhibit Estradiol (E2) Induced Proliferations in Breast Cancer Cells

Four cycloartane type triterpenoids and three stigmastane type steroids were isolated from the methanolic extract of stem and root part of Pothos scandens L. (Araceae), a Bangladeshi medicinal plant by high performance liquid chromatographic technique. The compounds were characterized as 24-methylenecycloartanol (1), 24-methylenecycloartenone (2), 24-en-cycloartenone (3), 24-methylenecycloartanyl ferulate (4), stigmast-4-en-3-one (5), stigmast-4,22-diene-3-one (6) and β-sitosterol glucoside (7) through extensive 1D and 2D NMR spectroscopic studies. All the isolates were evaluated for their estrogenic/antiestrogenic activity using the estrogen-responsive breast cancer cell lines, MCF-7 and T47D. The results showed that all the compounds possess mild to strong antiestrogenic activity in both cell lines which was compared to positive control tamoxifen. 24- Methylenecycloartanol (1), which contains a hydroxyl group in its C-3 position, inhibited 90% of estradiol (E2)- induced cell proliferation in MCF-7 and T47D cell lines at a concentration of 0.01 μM only. 24- Methylenecycloartanyl ferulate (4) and stigmast-4,22-diene-3-one (6) showed 90% of estradiol (E2)-induced cell proliferation in T47D cell only at a concentration of 0.01 μM whereas 10.0 μM was required for 24- methylenecycloartanyl ferulate (4) for the same activity in MCF-7 cells. This is the first report of isolation of these compounds from the plant along with their antiestrogenic property. Dhaka Univ. J. Pharm. Sci. 18(1): 93-102, 2019 (June)
Middleware Distributed Approach to Synchronous Detection of ARP Cache Poisoning

ARP cache poisoning based attack has been one of the most successful attack methods for years inside a LAN. There are a few solutions to detect and sometimes prevent an ARP based attack but they have some restrictions. In this paper we present a novel way to detect ARP cache poisoning inside a LAN. We propose a middleware and synchronous solution that has to be implemented in a distributed approach. Our solution requires no need have access and change to any Operating System code, but needs to be activated in timely manner and more than one host inside a LAN will be utilized to detect ARP cache poisoning based attack.
Homogeneous Catalyst Recycling and Separation of a Multicomponent Mixture Using Organic Solvent Nanofiltration

In homogeneous catalysis, the application of organic solvent nanofiltration (OSN) has become a well-known alternative to common recycling methods. Even though some OSN membranes are commercially available, their classification and the scope of application have to be determined for the specific solvent mixture. The commercial membrane Evoniks DuraMem (cid:2) 300 was tested in a mixture of ethanol, ethyl acetate, and cyclohexane with magnesium triflate as possible catalyst. The cross permeate fluxes were measured for two transmembrane pressures and the hydrodynamic radii of the components were determined. Some of the components in the ternary mixture are retained, which makes the membrane also suitable for fractioning thereof.
Anti inflammatory activity of Silver nanoparticles synthesised using Cumin oil

Aim: To evaluate the anti inflammatory activity of silver nanoparticles synthesised using Cumin oil. Introduction: Silver nanoparticles are today a standout amongst the most generally utilised Nanoparticles both in key therapeutic sciences and clinical practice. Silver particles are connected to modified cell demise, and expanded cytotoxicity in specific conditions. Materials and method: Preparation of denaturation particles using Cumin oil and evaluating the anti inflammatory activity by using albumin denaturation assay technique. Results: The characteristic surface plasmon absorption band were observed at 440nm. It is observed that with the increase in concentration, the anti-inflammatory activity of silver nanoparticles increases. Conclusion: Silver nanoparticles synthesized using cumin oil exhibit potent anti inflammatory activity and hence can be used along with anti inflammatory drugs, which would potentiate the effect of the drugs.
Leveraging semantic annotations for event-focused search & summarization

Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.
Proactive Detection of Unknown Binary Executable Malware

To detect unknown malware, heuristic methods or more generally statistical approaches are the most promising research trends nowadays, but their computing and detection performances are generally not compatible with what users do accept. Hence, most com- mercial AV products still heavily rely on signature-based detection (opcodes, control flow graph, and so on). This implies that frequent and prior updates must be performed. May their analysis techniques be fully static of dynamic (using sandboxing or virtual machines), commercial AVs do not capture what defines malware compared to benign files: their intrinsic actions. In this chapter, we focus on binary executables and we describe how to effectively synthetize these actions and what are the differences between malware and nonmalicious files. We extract and analyze two tables that are present in executable files: the import address table (IAT) and export address table (EAT). These tables summarize the different interactions of the executable with the operating system. We show how this information can be used in supervised learning to provide effective detection algorithms, which have proven to be very accurate and proactive with respect to unknown malware detection.
Patterns of seed dispersal and pollen flow in Quercus garryana (Fagaceae) following post‐glacial climatic changes

Aim We examined the genetic structure of Quercus garryana to infer post‐glacial patterns of seed dispersal and pollen flow to test the hypotheses that (1) peripheral populations are genetically distinct from core populations and from one another; (2) genetic diversity declines towards the poleward edge of the species’ range; and (3) genetic diversity in the chloroplast genome, a direct measure of seed dispersal patterns, declines more sharply with increasing latitude than diversity in the nuclear genome. We address our findings in the context of known historical oak distribution from pollen core data derived from previously published research.
Biometric recognition for kids

The idea of fusing multiple biometric modalities for the identiﬁcation of an individual has received signiﬁcant attention in the last two decades. However, this has been limited to recognizing adults only. With the quality of biometric hardware and sensors improving every year, it is surprising that this idea has not been explored for children within the age range of 2-5 years. In this project, we analyze whether we can use face, ﬁngerprint and iris to identify children. In this semester, we collected the 2nd session of the database comprising ﬁngerprint, face photo and iris sample for kids between age 2-4 years. We have used commercial-off-the-shelf tools (COTs) for iris and ﬁngerprint to evaluate our database. Further, we have tried to improve the performance, through modality fusion.
Toward a High-Order Preserving Sliding-Mesh Approach for Computational Aeroacoustics in Subsonic Turbomachinery

The aeroacoustic analogy is the hybrid technique most used to predict acoustic noise of open rotor turbo-machines. However it is not appropriate to turbo-machines operating in confined spaces - where the diffractions, reflections and near-field flow effects can not be neglected. To overcome this issue, acoustic sources are propagated using linearized Euler equation and rotor motion is taken into account by using a sliding mesh method. This technique allows relative sliding of one grid adjacent to another grid. In addition, we propose a high-order finite volume solver (based on Moving Least Squares approximation MLS), which works on unstructured grid, in order to address most of the industrial applications and their complex geometries. In this paper, two families of MLS-based sliding mesh method are presented. A comparison between the accuracy of the different sliding mesh approaches is made by using a theoretical acoustic benchmark.
Polymer microneedles integrated with glucose-responsive mesoporous bioactive glass nanoparticles for transdermal delivery of insulin

The traditional subcutaneous or intravenous injection of insulin can not effectively control the release of insulin, resulting in an unregulated the blood glucose levels on diabetic patients. It is urgently to develop a self-controllable insulin delivery system with high sensitivity to the change of blood glucose levels in vivo. In this work, polymer microneedles (MNs) integrated with glucose-responsive mesoporous bioactive glass nanoparticles (BGNs) for transdermal delivery of insulin had been developed for the self-regulate and painless transdermal administration. The hypoglycaemic drug (insulin) was loaded in the mesoporous BGNs that surface premodification by a composite enzyme layer (CEL). The CEL was consisted of polyethyleneimine (PEI), glucose oxidase (GOx) and catalase (CAT). The formed CEL could be used as the glucose-sensitive layer and ‘gatekeeper’ for the release of encapsulated insulin from mesoporous of BGNs. The glucose in CEL that diffused and penetrated from body fluids could be catalyzed to gluconic acid by GOx/CAT which caused the pH decrease in the CEL, resulting the structure destruction of CEL and leading to release of preloaded insulin from the BGNs. After the treatment of the as-prepared MNs on diabetic rats, a highly hypoglycemic effect and lower risk of hypoglycemia could be obtained. This study demonstrated that as-designed glucose-sensitive and pH-triggered transdermal delivery system was a promising strategy for the treatment of the diabetes.
Robust Network Architecture Against Random Threats in WMD Environments: Theoretical Limits and Recovery Strategies

Abstract : This project presents a comprehensive summary of the problems under study, our approaches, technical contributions, and accomplishments that are supported throughout the entire project period, April 15, 2008 to July 31, 2014. There are two time periods for this project, one is the original award from September 1 2008 to August 31, 2011, and the other from September 1, 2011 to July 31, 2014 as the supplemental period. In the original project, we addressed two thrusts, namely network vulnerabilities and recovery strategies in the aftermath of WMD attacks. More specifically, this research targets a set of fundamental issues to understand network response following an attack by WMD/WME: how to model a network topology in the presence of attacks/failures from random threats; how to estimate or predict network survivability to sustain critical applications; how to de-sign/form network architecture to approach the theoretical limits of network robustness; and how to inter-operate with other available/limited sources for fast communication recovery.
Stabilization/Solidification Treatment
of Cadmium-Bearing-Residue
with Magnesium Slag

Stabilizing/Solidifying cadmium in residue is a great challenge to the treatment of solid waste. In this work, a new stabilization process was proposed to dispose of simulated cadmium-bearing-residue (CBR) by using magnesium slag of the same high-hydration and gelling properties as a binder. A sharp decline in cadmium leaching contraction from 1415 mg·L-1 to less than 1 mg·L-1 was obtained in this new process. The XRD, FT-IR and XPS results indicated it was hydration products and silicates produced by magnesium slag that could promote cadmium (Cd) to take place of calcium (Ca) and immobilize cadmium in the residue. The European Community Bureau of Reference (BCR) experiment also discovered that more reducible, oxidizable and residual fraction in favour of restrained environmental availability was produced in the process.