Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of fire retardants based on N and P and poly(sodium silicate-aluminum dihydrogen phosphate) (PSADP) and testing the flame-retardant properties of PSADP impregnated poplar wood

Abstract The development of fire retardants (FRs) is an approximative process of optimization. In this context, a novel water-soluble formulation with poly(sodium silicate-aluminum dihydrogen phosphate) (PSADP) has been developed, aiming at reduced hygroscopicity and enhanced leaching resistance of poplar wood in combination with nitrogen-phosphorus (NP) FR (FRNP). After treatment of wood with FRNP and PSADP in vacuum, the following data of the samples were determined: rate of hydroscopicity, leaching resistance, heat release rate (HRR), total HR (THR), effective combustion heat, mass loss, and concentration of flue gas. FR distribution in the wood’s inner surface was investigated by scanning electron microscopy (SEM) and energy-dispersive X-ray analysis (EDXA). Results show that PSADP and FRNP have favorable synergistic effects on moisture resistance and flame retardance. Smoke density (SD) of NP and PSADP treated samples shows a significant reduction relative to that of NP. NP-PSADP treated samples form more char with carbon layers of higher density. At the same time, FRNP-PSADP is evenly distributed over the inner wood surfaces and penetrates the cell cavities of the poplar wood.
Reliability Analysis of Slotted Aloha with Capture for an OWC-based IoT system

In this article, we consider a random access scheme for an indoor Internet of Things (IoT) framework that uses optical wireless communication (OWC). We focus on a Slotted ALOHA (SA)-based solution where a number of OWC IoT users contend to send data to a central OWC receiver. In any given slot, and for a randomly selected active user, we consider the reliability of decoding the user's data packet at the receiver. This is done by deriving the signal-to-noise-and-interference-ratio (SINR) statistics from a randomly chosen user and evaluating the probability that the user's SINR is below a given threshold. By placing our analysis in the context of an indoor OWC IoT uplink setup, and employing the standard OWC channel model, we investigate the trade-offs between the reliability and the OWC system parameters such as the cell area or the transmitter's semiangle. We obtain valuable insights into the design of an SA-based random access solution for a typical indoor OWC cell.
Auto-detecting groups based on textual similarity for group recommendations

In general, recommender systems are designed to provide personalized items to a user. But in few cases, items are recommended for a group, and the challenge is to aggregate the individual user preferences to infer the recommendation to a group. It is also important to consider the similarity of characteristics among the members of a group to generate a better recommendation. Members of an automatically identified group will have similar characteristics, and reaching a consensus with a decision-making process is preferable in this case. It requires users-items and their rating interactions over a utility matrix to auto-detect the groups in group recommendations. We may not overlook other intrinsic information to form a group. The textual information also plays a pivotal role in user clustering. In this paper, we auto-detect the groups based on the textual similarity of the metadata (review texts). We consider the order in user preferences in our models. We have conducted extensive experiments over two real-world datasets to check the efficacy of the proposed models. We have also conducted a competitive comparison with a baseline model to show the improvements in the quality of recommendations.
Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.
Faraday–Fresnel rotation and splitting of orbital angular momentum carrying waves in a rotating plasma

Rotational Fresnel drag – or orbital Faraday rotation – in a rotating magnetised plasma is uncovered and studied analytically for Trivelpiece–Gould and whistler–helicon waves carrying orbital angular momentum (OAM). Plasma rotation is shown to introduce a non-zero phase shift between OAM-carrying eigenmodes with opposite helicities, similarly to the phase shift between spin angular momentum eigenmodes associated with the classical Faraday effect in a magnetised plasma at rest. By examining the dispersion relation for these two low-frequency modes in a Brillouin rotating plasma, this Faraday–Fresnel rotation effect is traced back to the combined effects of Doppler shift, centrifugal forces and Coriolis forces. In addition, the longitudinal group velocity in the presence of rotation is shown to depend both on rotation and azimuthal mode, therefore predicting the Faraday–Fresnel splitting of the envelope of a wave packet containing a superposition of OAM-carrying eigenmodes with opposite helicities.
Forgetting in Answer Set Programming - A Survey

Forgetting – or variable elimination – is an operation that allows the removal, from a knowledge base, of middle variables no longer deemed relevant. In recent years, many different approaches for forgetting in Answer Set Programming have been proposed, in the form of specific operators, or classes of such operators, commonly following different principles and obeying different properties. Each such approach was developed to address some particular view on forgetting, aimed at obeying a specific set of properties deemed desirable in such view, but a comprehensive and uniform overview of all the existing operators and properties is missing. In this article, we thoroughly examine existing properties and (classes of) operators for forgetting in Answer Set Programming, drawing a complete picture of the landscape of these classes of forgetting operators, which includes many novel results on relations between properties and operators, including considerations on concrete operators to compute results of forgetting and computational complexity. Our goal is to provide guidance to help users in choosing the operator most adequate for their application requirements.
Microbial Degradation of Polycyclic Aromatic Hydrocarbons

土壌よりピレンを資化するグラム陽性桿菌H2-5株を単離し, 土壌中にPAH類を分解する微生物が存在する事を示した。 本菌は抗酸性染色陽性で, 生育の最適温度は34. 3℃, 上限温度は45℃付近であった。 TSB培地で生育したH2-5株は菌体 (乾燥重量) 濃度45μg/mlで低濃度 (1.3μg/ml) のピレンを12時間の処理で90%分解し, 同濃度のBaP, BaA, およびBghiPを3日間の処理で, それぞれ60%, 25%, および8%分解した。 また, 高濃度 (26.7μ9/ml) のPAHでは, TSB培地で生育したH2-5株は菌体濃度226μg/mlでピレンを11日の処理で80%以上分解し, BaP, BaA, およびBghiPを同様の処理で, それぞれ80%以上, 40%, および30%分解した。 ピレン-無機液体培地で生育した, 混在菌を含むH2菌 (菌体濃度5.4μg/ml) による高濃度のピレンの処理では, 上記TSB培地で生育したH2-5株に比べ, 初期の分解速度は高かったが, 後期では遅くなり, 11日目の分解率に差はみられなかった。
Convolutional Neural Bandit: Provable Algorithm for Visual-aware Advertising

Online advertising is ubiquitous in web business. Image displaying is considered as one of the most commonly used formats to interact with customers. Contextual multi-armed bandit has shown success in the application of advertising to solve the exploration-exploitation dilemma existed in the recommendation procedure. Inspired by the visual-aware advertising, in this paper, we propose a contextual bandit algorithm, where the convolutional neural network (CNN) is utilized to learn the reward function along with an upper conﬁdence bound (UCB) for exploration. We also prove a near-optimal regret bound ˜ O ( √ T ) when the network is over-parameterized and establish strong connections with convolutional neural tangent kernel (CNTK). Finally, we evaluate the empirical performance of the proposed algorithm and show that it outperforms other state-of-the-art UCB-based bandit algorithms on real-world image data sets.
Deformable Elasto-Plastic Object Shaping using an Elastic Hand and Model-Based Reinforcement Learning

Deformable solid objects such as clay or dough are prevalent in industrial and home environments. However, robotic manipulation of such objects has largely remained unexplored in literature due to the high complexity involved in representing and modeling their deformation. This work addresses the problem of shaping elasto-plastic dough by proposing to use a novel elastic end-effector to roll dough in a reinforcement learning framework. The transition model for the end-effector-to-dough interactions is learned from one hour of robot exploration, and doughs of different hydration levels are rolled out into varying lengths. Experimental results are encouraging, with the proposed framework accomplishing the task of rolling out dough into a specified length with 60% fewer actions than a heuristic method. Furthermore, we show that estimating stiffness using the soft end-effector can be used to effectively initialize models, improving robot performance by approximately 40% over incorrect model initialization.
Ampicillin-Ceftriaxone vs Ampicillin-Gentamicin for Definitive Therapy of Enterococcus faecalis Infective Endocarditis: A Propensity Score–Matched, Retrospective Cohort Analysis

Abstract Background Ampicillin-ceftriaxone (AC) has emerged as an alternative antibiotic regimen for enterococcal infective endocarditis (EIE) with reduced toxicity compared with ampicillin-gentamicin (AG), but evidence regarding its success in reducing EIE-associated death in the United States is limited. Methods We conducted a retrospective, propensity score–matched cohort analysis of EIE patients treated with AC or AG between 2010 and 2017 at 3 hospitals in Pittsburgh, Pennsylvania. We assessed all-cause 90-day mortality as the primary outcome and in-hospital mortality, length of hospital stay, hospital readmissions, adverse events, and relapse of bacteremia as the secondary outcomes. Results A total of 190 patients with EIE (100 treated with AC and 90 with AG) were included. Ninety-day mortality was significantly higher with AC than AG (21% vs 8%; P = .02). After propensity score matching, 56 patients in each group remained for the outcomes analysis. Documented aminoglycoside resistance, presence of annular or aortic abscess, and complete pacemaker removal were the significantly different variables between the 2 matched cohorts. We observed no statistically significant difference in 90-day mortality between the 2 treatment groups (11% vs 7%; P = .55). Adverse events were more common in patients treated with AG (25 vs 39; P = .0091), and more patients in the propensity score–matched AG cohort switched antibiotic regimens than in the AC group (10% vs 49%; P < .0001). Conclusions Patients treated with AC demonstrate no significant differences in mortality, treatment failure, or bacteremia relapse compared with AG in a propensity score–matched EIE cohort.
An Intention Mining Approach using Ontology for Contextual Recommendations

Most modern Information Systems record user activity logs to extract knowledge and support new and higher quality services. Recommender systems (RS) in particular those based on collaborative filtering, are an example of such new services that have gained tremendous popularity. However, we know that standard activity logs are too poor to properly reflect user activity as they are influenced by many environmental factors (time, location, weather, profile, etc.), out of which a lot is not grasped in standard user activity logs. This makes it very difficult to mine users' intentions, which are key to analyze users' behavior reliably, and therefore key to quality recommendations. Several types of sources, such as sensors, external systems, outside actors, domain knowledge bases, or forecast systems could be used to obtain this information. The theory in this PhD is that the combination of multiple types of sources can extensively contribute to providing better insight into user activity through context-rich intentional process mining and therefore deliver newer and higher quality recommendations. While existing research mainly focuses on log datasets; only a few contributions consider ontologies to gather multiple sources. We propose a novel approach that (a) combines different types of sources into ontologies (b) uses such ontologies for intentional process mining (c) exploits this intentional model for contextual recommendations.
Electrophilically Activated Nitroalkanes in Synthesis of 3,4-Dihydroquinozalines

Nitroalkanes activated with polyphosphoric acid serve as efficient electrophiles in reactions with various nucleophilic amines. Strategically placed second functionality allows for the design of annulation reactions enabling preparation of various heterocycles. This strategy was employed to develop an innovative synthetic approach towards 3,4-dihydroquinazolines from readily available 2-(aminomethyl)anilines.
NLRP3 is dispensable for D-galactosamine/Lipopolysaccharide-induced fulminant hepatitis

Background: The NOD-like receptor family protein 3 (NLRP3) inflammasome is involved in the progression of liver inflammation. NLRP3 inactivation has been shown to protect mice against multiple types of experimental liver injury. However, it is unclear whether NLRP3 contributes to D-Galactosamine (DGalN) plus lipopolysaccharide (LPS) induced fatal hepatitis. This study aims to examine the function of NLRP3 inflammasome in DGalN/LPS induced acute liver failure.Method: The expression of inflammasomes was detected by quantitative PCR. Wild type (WT) mice and NLRP3 knockout mice were given an intraperitoneal injection of DGalN plus LPS. Liver damage was examined at 6 h post DGalN/LPS administration by histological analysis and serum aminotransferases measurement. Hepatocyte apoptosis and pyroptosis was detected with TUNEL assay, immunohistochemistry (IHC) staining, and western blot. Hepatic macrophages (F4/80+) and neutrophils (Ly6G+) were analyzed by IHC staining. The proinflammatory cytokines levels in serum and liver were examined using cytometric bead array and quantitative PCR. Hepatic IL1β level was further detected by western blot.Result: The hepatic NLRP3 activity was upregulated in WT mice treated with DGalN/LPS. Nlrp3−/− and WT mice showed similar mortality against lethal dose of DGalN/LPS. Serum aminotransferases levels and liver necrosis area of Nlrp3−/− mice did not differ from that in WT mice after DGalN/LPS injection. The numbers of liver TUNEL positive cells and cleaved caspase3 positive hepatocytes were comparable between Nlrp3−/− and WT mice. The hepatic GSDMDNterm peptide production also showed no difference between Nlrp3−/− and WT mice. Mice treated with DGalN/LPS displayed significantly increased numbers of intrahepatic F4/80+ cells and Ly6G+ cells, but the cell numbers in Nlrp3−/− mice livers were similar to those in WT mice. Consistently, serum and hepatic TNFα, IL6, and MCP-1 levels were similar between Nlrp3−/− and WT mice upon DGalN/LPS administration, but serum IL1β and hepatic mature IL1β level in Nlrp3−/− mice was reduced.Conclusions: NLRP3 ablation does not protect mice from DGalN/LPS induced acute liver failure, nor affect hepatocyte apoptosis and pyroptosis. Deficiency of NLRP3 has a limited effect on intrahepatic inflammatory response induced by DGalN/LPS treatment. NLRP3 inflammasome does not appear to be a major contributor to DGalN/LPS induced fatal hepatitis.
Unsupervised Hyperspectral Band Selection With Multigraph Integrated Embedding and Robust Self-Contained Regression

Band selection is an effective means to alleviate the curse of dimensionality in hyperspectral data. Many methods select a compact and low redundant band subset, which is inadequate as it may degrade the classification performance. Instead, more emphasis shall be put on selecting representative bands. In this article, we propose a robust unsupervised band selection method to address this issue. Our method reveals bandwise representativeness based on the comprehensive interband neighborhood structure. It incorporates an interband neighborhood graph into a sparse self-contained regression model in order to provide a reasonable measure for bandwise representativeness. The derived coefficient matrix not only uncovers bandwise importance values but also is coherent to the generalized interband local neighborhood structure. For constructing the interband neighboring structural graph, an integrated multigraph model is employed to achieve better generalization performance. It combines the benefit of multiple graphs but is insusceptible to the defects of a single one. To enhance the reliability of this model, a joint trace minimum and nonnegative constraint is imposed on the coefficient matrix. Accordingly, a multigraph integrated embedding and robust self-contained regression model (MGRSR) is formulated. In addition, an iterative update algorithm is developed to solve the problem. Comparative experiments on three hyperspectral data sets illustrate that MGRSR is robust to various data and has superior performance compared with several state-of-the-art methods.
VTR: an algorithm for identifying analogous contacts on protein structures and their complexes

Evolutionarily related proteins can present similar structures but very dissimilar sequences. Hence, understanding the role of the inter-residues contacts for the protein structure has been the target of many studies. Contacts comprise non-covalent interactions, which are essential to stabilize macromolecular structures such as proteins. Here we show VTR, a new method for the detection of analogous contacts in protein pairs. VTR performs structural alignment between proteins and detects interactions that occur in similar regions. To evaluate our tool, we proposed three case studies: (i) we compared a vertebrate myoglobin and a truncated invertebrate hemoglobin; (ii) analyzed interactions between the spike protein RBD of SARS-CoV-2 and the cell receptor ACE2; and (iii) compared a glucose-tolerant and a non-tolerant β-glucosidase enzyme used for biofuel production. The case studies demonstrate the potential of VTR for the understanding of functional similarities between distantly sequence-related proteins, as well as the exploration of important drug targets and rational design of enzymes for industrial applications. We envision VTR as a promising tool for understanding differences and similarities between homologous proteins with similar 3D structures but different sequences. VTR is available at http://bioinfo.dcc. ufmg.br/vtr.
Mapping the Effects of Anthropogenic Activities in the Catchment of Weija Reservoir using Remote Sensing Techniques

Man has contributed to land cover alteration since time-immemorial through clearing of land for residential, agriculture, recreational and industrial purposes. The emergence of adapting wild plants and animals for human use as well as industrialisation have also contributed to the alteration of land cover. Over the years, anthropogenic activities have had great impact on the Weija catchment. This study seeks to map the catchment and determine the impact of anthropogenic activities using Remote Sensing techniques. Observations and measurements were made on the field as well as classification of land cover using Landsat images of years 1991, 2003 and 2017. Results showed an increase in built-up areas by 18% from 1991 to 2017. Other classes such as shrubs increased due to decrease in dense vegetation. This study confirms the use of Remote Sensing as a valuable tool for detecting change in land cover and determining the impact of anthropogenic activities in the Weija Catchment. Keywords : Land Cover, GIS, Remote Sensing, Weija Catchment, Anthropogenic Activities
Ju l 2 02 1 Intersection Problems in Extremal Combinatorics : Theorems , Techniques and Questions Old and New .

The study of intersection problems in Extremal Combinatorics dates back perhaps to 1938, when Paul Erdős, Chao Ko and Richard Rado proved the (first) ‘Erdős-Ko-Rado theorem’ on the maximum possible size of an intersecting family of k-element subsets of a finite set. Since then, a plethora of results of a similar flavour have been proved, for a range of different mathematical structures, using a wide variety of different methods. Structures studied in this context have included families of vector subspaces, families of graphs, subsets of finite groups with given group actions, and of course uniform hypergraphs with stronger or weaker intersection conditions imposed. The methods used have included purely combinatorial methods such as shifting/compressions, algebraic methods (including linear-algebraic, Fourier analytic and representation-theoretic), and more recently, analytic, probabilistic and regularity-type methods. As well as being natural problems in their own right, intersection problems have revealed connections with many other parts of Combinatorics and with Theoretical Computer Science (and indeed with many other parts of Mathematics), both through the results themselves, and the methods used. In this survey paper, we discuss both old and new results (and both old and new methods), in the field of intersection problems. Many interesting open problems remain; we will discuss several such. For expositional and pedagogical purposes, we also take this opportunity to give slightly streamlined versions of proofs (due to others) of several classical results in the area. This survey is intended to be useful to graduate students, as well as to more established researchers. It is an expanded version of a survey paper that will appear in the Proceedings of the 29th British Combinatorial Conference, University of Lancaster, 11th-15th July 2022.
The Effect of Obstructive Sleep Apnea and Continuous Positive Airway Pressure Therapy on Skeletal Muscle Lipid Content in Obese and Nonobese Men

Abstract Obstructive sleep apnea (OSA), independently of obesity (OBS), predisposes to insulin resistance (IR) for largely unknown reasons. Because OSA-related intermittent hypoxia triggers lipolysis, overnight increases in circulating free fatty acids (FFAs) including palmitic acid (PA) may lead to ectopic intramuscular lipid accumulation potentially contributing to IR. Using 3-T-1H-magnetic resonance spectroscopy, we therefore compared intramyocellular and extramyocellular lipid (IMCL and EMCL) in the vastus lateralis muscle at approximately 7 am between 26 male patients with moderate-to-severe OSA (17 obese, 9 nonobese) and 23 healthy male controls (12 obese, 11 nonobese). Fiber type composition was evaluated by muscle biopsies. Moreover, we measured fasted FFAs including PA, glycated hemoglobin A1c, thigh subcutaneous fat volume (ScFAT, 1.5-T magnetic resonance tomography), and maximal oxygen uptake (VO2max). Fourteen patients were reassessed after continuous positive airway pressure (CPAP) therapy. Total FFAs and PA were significantly (by 178% and 166%) higher in OSA patients vs controls and correlated with the apnea-hypopnea index (AHI) (r ≥ 0.45, P < .01). Moreover, IMCL and EMCL were 55% (P < .05) and 40% (P < .05) higher in OSA patients, that is, 114% and 103% in nonobese, 24.4% and 8.4% in obese participants (with higher control levels). Overall, PA, FFAs (minus PA), and ScFAT significantly contributed to IMCL (multiple r = 0.568, P = .002). CPAP significantly decreased EMCL (–26%) and, by trend only, IMCL, total FFAs, and PA. Muscle fiber composition was unaffected by OSA or CPAP. Increases in IMCL and EMCL are detectable at approximately 7 am in OSA patients and are partly attributable to overnight FFA excesses and high ScFAT or body mass index. CPAP decreases FFAs and IMCL by trend but significantly reduces EMCL.