Miracle-dz/UniFAR-FTUs huggingface dataset:
The Beggar as a Political Symbol: An Interactionist Reading of the Endurance of Anti-Begging Laws in India

In 2018, the Delhi High Court held that certain provisions of the state’s anti-begging law were unconstitutional. Nevertheless, such laws continue to operate in at least 20 other Indian states and union territories even today. Begging as a social phenomenon remains an under-researched subject within the social sciences, especially in India where the rare mention that the subject finds often gets subsumed within larger debates on chronic poverty or organised crime. This article begins by tracing the history of regulations around begging, followed by a discussion on the persistence of both begging and anti-begging laws prevalent today. By examining the justification underlying the criminalisation of begging, it contends that such an approach fails to provide insight into the lived experiences of individuals engaged in this activity. It therefore proposes that the analyses of begging in the Indian context adopt symbolic interactionism that lends its rich theoretical framework to enable an interpretation of the act as one of agency; a survival strategy among those living on the margins of the neoliberal urban experience. In doing so, it posits a view of the beggar as a powerful political symbol with the potential to subvert and interrogate the rules of the game in a globalised world.
Formation of Biofilms in a Biofilm Air-Lift Suspension Reactor

Biofilm Air-lift Suspension reactors can be used to treat waste water at a high volumetric loading rate combined with a low sludge loading. The biofilms are formed on small suspended particles (r = 0.1 mm). We have studied the effect of particle characteristics and hydraulic retention time on the biofilm formation. It is shown that small, rough particles show the best biofilm formation. Low hydraulic retention times benefit the formation of biofilms. This results from the fact that suspension growth is minimal under these conditions. The effect of biofilm detachment became distinct from the observation that most of the bacterial growth in the biofilm is transferred to the liquid. The biofilm formation process is concluded to be a three stage process: (i) the outgrowth of single cells to micro-colonies, this process is positively influenced by the carrier surface roughness; (ii) the outgrowth of micro-colonies to small biofilms, this process is negatively influenced by the concentration of carrier material; (iii) the outgrowth of biofilms, this occurs when the majority of particles are covered with a biofilm. At that time the influence of shear due to particle-particle interactions diminishes.
Development and validation of a nomogram for predicting severity in patients with hemorrhagic fever with renal syndrome: A retrospective study

Abstract Background Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by hantavirus infection. Patients with severe HFRS may develop multiple organ failure or even death, which makes HFRS a serious public health problem. Methods In this retrospective study, we included a total of 155 consecutive patients who were diagnosed with HFRS, of whom 109 patients served as a training cohort and 46 patients as an independent verification cohort. In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables of the risk model. Multivariate logistic regression analysis was used to construct a nomogram containing the characteristic variables selected in the LASSO regression model. Results The area under the receiver operating characteristic curve (AUC) of the nomogram indicated that the model had good discrimination. The calibration curve exhibited that the nomogram was in good agreement between the prediction and the actual observation. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. Conclusion In this study, we established a simple and feasible model to predict severity in patients with HFRS, with which HFRS would be better identified and patients can be treated early.
Anxiety, depression, and glycemic control during Covid-19 pandemic in youths with type 1 diabetes

Abstract Objectives Our study aims to assess the impact of lockdown during the coronavirus disease 2019 pandemic on glycemic control and psychological well-being in youths with type 1 diabetes. Methods We compared glycemic metrics during lockdown with the same period of 2019. The psychological impact was evaluated with the Test of Anxiety and Depression. Results We analyzed metrics of 117 adolescents (87% on Multiple Daily Injections and 100% were flash glucose monitoring/continuous glucose monitoring users). During the lockdown, we observed an increase of the percentage of time in range (TIR) (p<0.001), with a significant reduction of time in moderate (p=0.002), and severe hypoglycemia (p=0.001), as well as the percentage of time in hyperglycemia (p<0.001). Glucose variability did not differ (p=0.863). The glucose management indicator was lower (p=0.001). 7% of youths reached the threshold-score (≥115) for anxiety and 16% for depression. A higher score was associated with lower TIR [p=0.028, p=0.012]. Conclusions Glycemic control improved during the first lockdown period with respect to the previous year. Symptoms of depression and anxiety were associated with worse glycemic control; future researches are necessary to establish if this improvement is transient and if psychological difficulties will increase during the prolonged pandemic situation.
Imitation Cheese Manufacture Using Rapid Visco-Analyzer and Its Optimization

Imitation cheeses in a laboratory-scale (100-g) made by a rapid visco analyzer (RVA) at different stirring speeds (200, 300, 450 rpm) were investigated with a control (Stephan cooker at 1,500 rpm) at 85 °C through functional properties, microstructure and sensory evaluation. Compared to the cheeses made by the Stephan cooker, the cheese made by RVA was significantly (P < 0.05) more green, more yellow and noticeably less white than all other cheeses, which had a decrease in the L* values and an increase in a* and b* values. TPA values of imitation cheese made by the RVA at 450 rpm were lower than that cheese made by other speed. However, the imitation cheeses of 450 rpm was significantly (P < 0.05) higher, which was similar with the fat leakage of imitation cheese manufactured by Stephan cooker. The melt of imitation cheese significantly (P < 0.05) reduced by increasing the stirring speed of RVA. While significant, the melt of the 1,500 rpm cheese was smaller than all other cheeses. Increasing the stirring speed of RVA showed a more effective fat emulsification and a uniform protein matrix. Sensory analysis showed the imitation cheese made by RVA at 450 rpm was similar to the control made by Stephan cooker. Subsequently, the stirring speed, mixing time and heating temperature were optimized at 450 rpm, 7.50 min and 86 °C by response surface methodology (RSM). Results indicated that RVA could make imitation cheeses as well as the functional properties of the control in a laboratory-scale.
Carbon nanostructure-based superhydrophobic surfaces and coatings

Abstract Research and development on superhydrophobic carbon nanostructures and their nanocomposites have high industrial significance. Here, a comprehensive review of the topic is provided. Reported works on superhydrophobic surfaces and coatings of carbon nanotubes, nanofibres, nanospheres/nanothorns/others, nanodiamond, fullerene and their various nanocomposites with metals, ceramics, and polymers are described. Superhydrophobic nanostructured carbon soot, graphitic carbon, and others are also presented. The section on superhydrophobic graphene is presented concisely at the end. Reports in different application areas, including anti-corrosion, anti-icing, oil separation, anti-biofouling, and sensors, are discussed separately. Superoleophobic and superamphiphobic surfaces are also discussed. Graphical abstract
Inertial focusing in planar pulsatile flows

Abstract Oscillatory flows have the potential to overcome long-standing limitations encountered when using steady flows for inertial focusing due to low particle Reynolds numbers. The periodic displacement generated by oscillatory flow increases the total path length travelled by a suspended particle with no net displacement within a channel or the need for increased channel length. The effects of unsteady inertial forces on inertial focusing, however, have not been thoroughly examined. Here, we present a combined theoretical and experimental study on the effect of Womersley number on inertial focusing in planar pulsatile flows. The migration velocity for a small and weakly inertial particle was evaluated for different oscillation frequencies using the method of matched asymptotics. Using experiments in a custom-built microchannel, we show that oscillatory flows are remarkably efficient for inertial focusing, even at low particle Reynolds numbers. For appropriately selected oscillation amplitude and frequency, inertial focusing is achieved in only a fraction of the channel length (1 % to 10 %) compared to what would be required in a steady flow. We show that the Womersley number influences the equilibrium focusing position and the overall focusing efficiency. In fact, above a critical Womersley number, inertial focusing does not occur despite increasing particle Reynolds number. Lastly, the application of oscillatory inertial focusing for the direct measurement of particle migration velocity is demonstrated.
A Byzantine Fault-Tolerant Consensus Library for Hyperledger Fabric

Hyperledger Fabric is an enterprise grade permissioned distributed ledger platform that offers modularity for a broad set of industry use cases. One modular component is a pluggable ordering service that establishes consensus on the order of transactions and batches them into blocks. However, as of the time of this writing, there is no production grade Byzantine Fault-Tolerant (BFT) ordering service for Fabric, with the latest version (v2.3) supporting only Crash Fault-Tolerance (CFT). In this work we describe the design and implementation of a BFT ordering service for Fabric, employing a new BFT consensus library. The new library, based on the BFT-SMART protocol and written in Go, is tailored to the blockchain use-case, yet is general enough to cater to a wide variety of other uses. The BFT library’s design and integration into Fabric address crucial aspects that were left unsolved in all prior work, making them unfit for production use. We evaluate the new BFT ordering service by comparing it with the currently supported Raft-based CFT ordering service in Hyperledger Fabric.
Design of a Switched-Mode Operational Amplifier Operating With a 0.5V Supply Voltage

This work presents a novel design of a switched-mode operational amplifier (SMOA). It consists of two stages. The first stage is a low supply voltage operational amplifier. It utilizes common mode feedback techniques to eliminate current sources and increase the output voltage swing. The second stage is a pulse width modulator (PWM) which transforms the output signal information from voltage to time domain. This results in increasing the voltage swing and reducing the distortion issues that conventional operational amplifiers suffer from. Multi-phase techniques were employed to attenuate the PWM distortion components and increase linearity. The 10-phase SMOA circuit proposed in this work was implemented and simulated using Hspice circuit simulator. It uses 28nm CMOS technology and operates from a 0.5V supply voltage and a 500MHz PWM frequency. It consumes 1.5mW while achieving a 740mV peak-to-peak differential output voltage swing with -43.9dB total harmonic distortion (THD) and 44.7dB spurious free dynamic range (SFDR) when a 1MHz input frequency is used.
GCN-ICF: Graph Convolution Networks for Item-based Recommendation

Item-based Collaborative Filtering (ICF) is wildly used recommendation method. Under the assumption that a user tends to choose an item similar to what she has interacted before, ICF uses items that a user has interacted to learn a user’s profile, and then makes recommendations by calculating the similarity between a target item and the user’s profile. Therefore, the method of learning item representations from data is crucial in ICF. However, most existing ICF methods only consider the low-order connectivity between a target item and a target user’s interacted items. The consideration limits the improvement of recommendation qualities. In this paper, by using the message-passing idea of Graph Convolution Networks (GCN), we propose GCN-ICF to capture the high-order connectivity between users and items in the user-item interaction bipartite graph structure. The method enriches the representation of each item through its neighbors in the bipartite graph structure. The experimental results on three public datasets verify the highly positive effect of the performance of the GCN-ICF method.
Towards a Privacy Respecting Image-based User Profiling Component

This paper explores the development of a framework for content-aware user profiling, studying how image producers and consumers can be better understood and consequently better served through services such as matchmaking and friend recommendations. User interests and similarities are extracted and analyzed on the edge employing state of the art CNN models over user images for the tasks of classification, as well as, building latent user representations from personal media content. A private-by-design approach is employed through the development and deployment of models on-device, avoiding the need for communicating personal data to a central service. Experimental results show that user profiling can provide accurate ranking of the users’ interests and meaningful user associations through profile similarity.
Comparative Study of Chemical Composition of Calotropis gigantea Flower, Leaf and Fruit Essential Oil

Essential oil from flowers, leaves and fruits of Calotropis gigantea (L), was extracted by hydro distillation method. Total 21 components in leaves, 43 components in flowers and 21 components in fruits were identified. The extracted oil was characterized by GC and GC-MS techniques. Constituents common to flower, leaves and fruit were 3-Hexen-1-ol, Benzaldehyde, Benzyl alcohol, ± Linalool, Oct-3-en-2-ol, Phenethyl alcohol, α- Terpineol, 2,4-dimethyl-Acetophenone, 4-vinyl Guaiacol and n-Tetradecane.
An O(n2) Self-Stabilizing Algorithm for Minimal Total Dominating Set in Arbitrary Graphs

Given a connected graph G=(V, E) (without any isolates), a set ${\mathcal {S}} \subseteq$V is a total dominating set if each node i$\in$V is adjacent to at least one node in $\mathcal{S}$. A total dominating set $\mathcal{S}$ is minimal iff there does not exist a node i$\in \mathcal{S}$ such that the set $\mathcal{S}$-{i} is a total dominating set. In this paper, we propose a new self-stabilizing algorithm for minimal total dominating set. The stabilization time is O(n2) steps using a central daemon, and the space requirement at each node is O(logn) bits for an arbitrary connected graph with n nodes. In the literature, the best reported stabilization time for a minimal total dominating set algorithm is O(n3) with the same space requirement at each node using central daemon.
A Resilient Consensus Protocol for Networks With Heterogeneous Confidence and Byzantine Adversaries

A class of Adversary Robust Consensus protocols is proposed and analyzed. These are inherently nonlinear, distributed, continuous-time algorithms for multi-agents systems seeking to agree on a common value of a shared variable, in the presence of faulty or malicious Byzantine agents, disregarding protocol rules and communicating arbitrary possibly differing values to neighboring agents. We adopt monotone joint-agent interactions, a general mechanism for processing locally available information and allowing cross-comparisons between state-values of multiple agents simultaneously. The topological features of the network are abstracted as a Petri Net and convergence criteria for the resulting time evolutions formulated in terms of suitable structural properties of its invariants (so called siphons). Finally, simulation results and examples/counterexamples are discussed.
Ether Price Prediction Using Advanced Deep Learning Models

Over the last years, cryptocurrencies have gained popularity as a means of exchange, but mostly as an investment asset that can yield important earnings. Accurate cryptocurrency price prediction is the holy grail of investors, yet the task is extremely complex and tedious since cryptocurrencies exhibit high volatility and steep fluctuations compared to fiat money, while they depend on a plethora of factors related to the blockchain network, market trends, social popularity and the prices of other (crypto)currencies. Thus, simple statistical methods are not able to capture the complexity of cryptocurrency exchange rate, forcing researchers to turn to advanced machine learning techniques. In this work, we present a methodology for building deep learning models to forecast the price of cryptocurrencies and apply it to the prediction of Ether price, resulting in short-and long-term forecasts that achieve an accuracy of up to 84.2%.
Exploring MPI Collective I/O and File-per-process I/O for Checkpointing a Logical Inference Task

We present a scalable parallel I/O system for a logical-inferencing application built atop a deductive database. Deductive databases can make logical deductions (i.e. conclude additional facts), based on a set of program rules, derived from facts already in the database. Datalog is a language or family of languages commonly used to specify rules and queries for a deductive database. Applications built using Datalog can range from graph mining (such as computing transitive closure or k-cliques) to program analysis (control and data-flow analysis). In our previous papers, we presented the first implementation of a data-parallel Datalog built using MPI. In this paper, we present a parallel I/O system used to checkpoint and restart applications built on top of our Datalog system. State of the art Datalog implementations, such as Soufflé, only support serial I/O, mainly because the implementation itself does not support many-node parallel execution.Computing the transitive closure of a graph is one of the simplest logical-inferencing applications built using Datalog; we use it as a micro-benchmark to demonstrate the efficacy of our parallel I/O system. Internally, we use a nested B-tree data-structure to facilitate fast and efficient in-memory access to relational data. Our I/O system therefore involves two steps, converting the application data-layout (a nested B-tree) to a stream of bytes followed by the actual parallel I/O. We explore two popular I/O techniques POSIX I/O and MPI collective I/O. For extracting performance out of MPI Collective I/O we use adaptive striping, and for POSIX I/O we use file-per-process I/O. We demonstrate the scalability of our system at up to 4,096 processes on the Theta supercomputer at the Argonne National Laboratory.
A Drug-Target Interaction Prediction Based on GCN Learning

In recent years, the use of deep learning methods for drug-target interaction (DTI) prediction has become the mainstream research direction. Drugs, targets, and other related biological and chemical properties have constructed a very complex network structure. How to effectively extract network features and predict target has become a big challenge. Graph Convolutional Neural Network (GCN) is one of the effective deep learning methods for complex networks. It extends the convolution operation from traditional European space to non-Euclidean space, and can simultaneously perform end-to-end node attribute information and structural information. End-to-end learning, its core idea is to learn a function mapping, through which nodes in the mapping graph can aggregate their own features and its neighbor features to generate a new representation of the node. In this study, we introduce the GCN link prediction method decagon for DTI prediction research. The experimental data comes from the DTI-net model. By combining the drug-drug interaction relationship matrix, the target-target interaction relationship matrix and the drug-target interaction relationship matrix provided by DTI-net, the drug characteristics and target characteristics are expressed as the attribute characteristics of the network nodes, thereby obtaining DTI Heterogeneous Network. In order to improve the ability to predict the drug-target relationship, this article has done a lot of tuning experiments in parameter selection and optimization strategies, and analyzed and compared the prediction results. The best predicted AUC is 0.919, and the best AUPR is 0.922. In terms of traditional drug-target prediction methods, the GCN method can effectively extract the features contained in heterogeneous networks, which proves the feasibility of this method in predicting drug-target interactions.
MoRAI: Geographic and Semantic Overlay Network for Linked Data Access with Intermittent Internet Connectivity

We propose and evaluate MoRAI (Mobile Read Access in Intermittent internet connectivity), a distributed peer-to-peer architecture organized in three levels dedicated to RDF data exchanges by mobile contributors. We present the conceptual and technical aspects of this architecture as well as a theoretical analysis of the different characteristics. We then evaluated it experimentally and results show the relevance of considering geographical positions during data exchanges and of integrating RDF graph replication to ensure data availability in terms of requests completion rate and resistance to crash scenarios.
Self-heating effect in nanoscale SOI Junctionless FinFET with different geometries

In this work we study the self-heating effect (SHE) in nanoscale Silicon on Insulator Junctionless (SOI JL) FinFET transistor with fin cross section in rectangular, trapeze and triangle form. The lattice temperature dependence on the channel length as well as on buried oxide thickness is considered. It is shown that for considered transistor structure the lattice temperature in the middle of the channel is lower than at lateral sides, near source and drain. Also, we have found at the same conditions the lattice temperature depends on shape of channel cross section too.