Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning from Synthetic Point Cloud Data for Historical Buildings Semantic Segmentation

Historical heritage is demanding robust pipelines for obtaining Heritage Building Information Modeling models that are fully interoperable and rich in their informative content. The definition of efficient Scan-to-BIM workflows represent a very important step toward a more efficient management of the historical real estate, as creating structured three-dimensional (3D) models from point clouds is complex and time-consuming. In this scenario, semantic segmentation of 3D Point Clouds is gaining more and more attention, since it might help to automatically recognize historical architectural elements. The way paved by recent Deep Learning approaches proved to provide reliable and affordable degrees of automation in other contexts, as road scenes understanding. However, semantic segmentation is particularly challenging in historical and classical architecture, due to the shapes complexity and the limited repeatability of elements across different buildings, which makes it difficult to define common patterns within the same class of elements. Furthermore, as Deep Learning models requires a considerably large amount of annotated data to be trained and tuned to properly handle unseen scenes, the lack of (big) publicly available annotated point clouds in the historical building domain is a huge problem, which in fact blocks the research in this direction. However, creating a critical mass of annotated point clouds by manual annotation is very time-consuming and impractical. To tackle this issue, in this work we explore the idea of leveraging synthetic point cloud data to train Deep Learning models to perform semantic segmentation of point clouds obtained via Terrestrial Laser Scanning. The aim is to provide a first assessment of the use of synthetic data to drive Deep Learning--based semantic segmentation in the context of historical buildings. To achieve this purpose, we present an improved version of the Dynamic Graph CNN (DGCNN) named RadDGCNN. The main improvement consists on exploiting the radius distance. In our experiments, we evaluate the trained models on synthetic dataset (publicly available) about two different historical buildings: the Ducal Palace in Urbino, Italy, and Palazzo Ferretti in Ancona, Italy. RadDGCNN yields good results, demonstrating improved segmentation performances on the TLS real datasets.
Analysis of Faults in Cyber-Physical Systems by Finite Discrete-Time Markov Chains

In the given paper the problem of Cyber-Physical Systems behavior analysis in the occurrence of faults is investigated. To present the fault-free behavior of the investigated Cyber-Physical System as well as its behaviors in the presence of admissible faults, some Finite Discrete-Time Markov Chain is proposed and analyzed. It is shown that a single stationary probability distribution exists for the proposed model. This distribution is applied for characterization the behaviors of the investigated Cyber-Physical System in terms of the distance from the current fault to the set of critical faults. Besides, the algorithm for bounded probabilistic reachability analysis of the target set of faults is proposed.
Columnar liquid crystals in oligosaccharide derivatives: II. Two types of discotic columnar liquid-crystalline phase of cellobiose alkanoates

Abstract A series of cellobiose octaalkanoates, Cel-II-n (n is the carbon number of the alkyl chain), with n = 7–14 were prepared and their mesogenic properties examined by differential scanning calorimetry, optical polarizing microscopy and X-ray diffraction. All of these compounds form enantiotropic discotic columnar phases, in which the columns are built up by a regular stacking of the cellobiose moieties and are packed in a two dimensional lattice. Homologues with n = 9–14 form the Dho phase only whilst the compound with n = 7 forms the Dro phase. The n = 8 compound forms the Dho phase at higher temperatures and the Dro phase at lower temperatures. Structural parameters obtained from X-ray diffraction studies are presented for both phases.
Thermoelectric power and electronic structure of dilute alloys of nickel and cobalt with d transition elements

We present new measurements of thermoelectric power for nickel and cobalt dilute alloys with d transition elements in two temperature ranges: from 1.3 to 4.2 K and from 77 to 300 K. We analyse the measurements at room temperature in a two-band conduction model and deduce the thermopowers associated with the matrix (Smσ) and with the impurities (Soσ) in each sub-band σ. The results obtained for the impurity thermoelectric power confirm the model of electronic structure suggested for this alloy from magnetization and resistivity measurements; this model is characterized by the occurrence of a virtual bound state supward arrowdupward arrow at the Fermi level when the charge difference between the matrix and the impurity is large enough. From the values of impurity thermoelectric power in the supward arrow sub-band, we deduce the position and the width of the virtual bound state.
Skin in the Game in the Originate-to-Distribute Model: Evidence from the Online Social Lending Market

This paper analyzes the certification mechanisms and incentives that enable lending markets to match demand and supply despite the absence of financial intermediaries with skin in the game. Our analysis of the online social lending market, in which there is no financial intermediary, shows that the creation of self-organized groups helps the market to work efficiently but allowing group leader rewards, similar to origination fees in securitization, is detrimental. We are able to take advantage of a natural experiment in which group rewards are eliminated to examine how the same groups behave once these origination fees are removed. In general, group leaders signal borrower quality to other lenders by endorsing and submitting bids for listings in their groups. Borrowers in these groups have a significantly higher likelihood of receiving a loan, pay significantly lower interest rates, and default less often than comparable borrowers outside groups. However, when group leaders receive an origination fee for successful loan listings, it creates adverse incentives so despite bids and endorsement, loans originated by such group leaders have higher default rates. Group leaders become more careful in screening after the elimination of these rewards, and if their loan participation is high, i.e. when they have skin in the game and are thus severely hurt by a borrower default. These results have important implications for the question of how lending markets can function properly and for the ongoing debate about the future of securitization and the originateto-distribute model.
Comparative evaluation of full‐scale UASB reactors treating alcohol distillery wastewaters in terms of performance and methanogenic activity

In this study, two full-scale upflow anaerobic sludge blanket (UASB) reactors, namely TUASB and CUASB, at the wastewater treatment plants of the Tekirdaǧ Alcohol (Raki) and Canakkale Alcohol (Cognac) distilleries were investigated in terms of performance, acetoclastic methanogenic capacity and microbial composition. The results were compared with a previously studied other UASB reactor (IUASB) at the wastewater treatment plant of the Istanbul Alcohol (Raki) Distillery from which the two reactors (TUASB and CUASB) were seeded. The IUASB reactor performed well achieving COD removal efficiencies of no lower than 85% at organic logding rates (OLRs) in the range of 6–11 kg COD m−3 day−1 between 1996 and 2001. During the last one year of operation, between 2000 and 2001, performance of the CUASB reactor in terms of COD removal efficiency was 70–80% at OLRs in a range of 1–4.5 kg COD m−3 day−1 whereas it was 60–80% at OLRs in a range of 2.5–8.5 kg COD m−3 day−1 in the TUASB reactor. At the end of year 2000, specific methanogenic activity (SMA) tests were carried out to determine potential loading capacity and optimum operating conditions of the IUASB, CUASB and TUASB reactors. The potential methane production (PMP) rates of the CUASB, IUASB and TUASB reactors were measured as 230 cm3 CH4 gVSS−1 day−1, 350 cm3 CH4 gVSS−1 day−1 and 376 cm3 CH4 gVSS−1 day−1 respectively. When the PMP rates were compared with actual methane production (AMP) rates obtained from the three UASB reactors, AMP/PMP ratios were evaluated to be 0.18, 0.12 and 0.13 for CUASB, TUASB and IUASB reactors respectively. This showed that the CUASB, TUASB and IUASB reactors were using only 18%, 12% and 13% of their potential acetoclastic methanogenic capacity respectively. These results can be interpreted that the three UASB reactors were underloaded compared with their potential acetoclastic methanogenic capacities. It was, therefore, recommended that the three UASB reactors should be loaded at higher organic loading rates or sludge withdrawn in order to maintain an AMP/PMP ratio of 0.6–0.7, which can ensure desired reactor performance with safer operation. Results of epifluoresence microscopic examinations showed that the percentage of total autofluorescent methanogens was approximately 30% of the total population in sludges from the TUASB and IUASB reactors whereas it was 20% in sludge from the CUASB reactor. The two UASB reactors treating raki distillery wastewaters contained sludges having a higher percentage of autofluorescent methanogenic population and higher acetoclastic methanogenic activity. Copyright © 2004 Society of Chemical Industry
Heart rate response differs between elite and non- elite Czech female basketball matches

The aim of this study was to analyze heart rate (HR) response to Czech female basketball matches played at a different competitive level. Additionally, the influence of a game position was evaluated. Heart rate (HR) was monitored in eight official matches in elite: first league (N=8, age= 22,7 ± 1,9 years) and national (nonelite: second league) (N=9, age= 24,1 ± 2,3 years) female players. The average HR in matches was 91.9 ± 5.3 % of HRmax at elite level and 88.8 % ± 4.2 % of HRmax at national level (P < .001; d = 0.58). This difference was more pronounced in forwards (P < .001, d = 1.2) than centers (P < .001, d = 0.41) and guards (P < .001, d = 0.18). In conclusion, these results allow improving game understanding and helping coaches to prescribe accurate practice sessions based on individual requirements.
Values gained and human development aspects of cheerleading

The study focused on the relationship between the effects of cheerleading participation and the values gained and aspects the of human development such as mental, physical, social, emotional, spiritual and aesthetic aspects among the cheerleading participants in Iligan City in the year 2008-2009. The respondents of the study were the cheerleading participants of the selected schools in Iligan City. The study utilized descriptive – correlation method of research. The data generated results that cheerleaders should balance their participation in cheerleading to avoid mental stress so as not to affect their studies. It is therefore, highly recommended that teachers must encourage students to join in a cheerleading team, especially the Physical Educators. It is also recommended that the administrators must give scholarship to the students who are qualified to join a cheerleading team.
RBF Bearing Fault Prediction Model Based on Phase Space Reconstruction

In view of the complex nonlinear characteristics of the temperature time series of the rolling bearing of the tank car, the reconstruction parameters (embedded dimension and delay time) are determined according to the improved C-C algorithm to reconstruct the phase space of the temperature data, and the optimal expansion rate is found by the particle swarm algorithm The factor establishes the Radial Basis Function Neural Network (RBF) for prediction. The test set verification is named PSR-RBF, and compared with the typical algorithms ARMA, LSTM, SVM, the result shows: PSR-RBF neural network model prediction error at about eight out of ten thousand, the PSR-RBF neural network model has higher prediction accuracy compared with the results of other algorithms.
A Rational Choice Theory Perspective of Deploying Honeypots to contain the Insider Threat

The aim of this paper is to provide an understanding of the insider threat using rational choice theory. Specifically, the decision-making process of a maleficent insider in the presence of a luring honeypot is reviewed. Understanding these decisions may assist in designing technological solutions that are deployed at opportune decision points to contain the insider threat. To test the premise, an insider threat mitigation strategy derived from the rational choice modeling perspective is presented.
EVALUATION OF MICROBIAL LOAD IN SOME CANNED FRUITS AND LINE PROCESSING OF CANNED ORANGE

The aim of this study was to evaluate the microbial load in lines producing canned orange at local plant. In addition, this study was conducted to give information about the quality (i.e., total viable count (TVC) and total yeasts and molds (TYM)), hygiene (coliforms and Clostridium spp.) and safety (i.e., Staphylococcus aureus and Escherichia coli) of some canned fruits kept on the shelf in markets. The final products of canned orange, grapefruit, peach, pineapple, cocktail and cherry had some TVC and TYM. The counts of these microbial populations ranged between 1.43 to 2.40 log cfu/g and1.0 to 1.85 log cfu/g, respectively. Clostridium spp. , Staph. aureus and E. coli were totally absent in all canned fruits. The majority of the sampling sites examined were moderately contaminated (1.0 to 2.21 log cfu/g or ml or cm2 or can) by spoilage bacteria (coliforms, Bacillus spp. Staphylococci, Pseudomonas spp., and other bacteria) and fungi (Penicillium, Aspergillus spp. and other fungi) by workers, tables, water and peeling machines. The presence of this flora seemed to be associated with high numbers of one or more specific groups of the house-flora on the sampling sites and personal hands that as source of contamination. These findings further indicate that inadequate hygiene practices within processing line may result in loss of microbial control. It was shown that the hygienic status of the processing environment and equipment play an essential role in the microbial stability and safety of the final products.
Machine Learning Approach for Multiple Coordinated Aerial Drones Pursuit-Evasion Games

This paper presents a machine-learning algorithm applied to a quadcopter application. We are proposing a fuzzy actor-critic learning (FACL) algorithm. This method enables a pursuer quadcopter to capture an evader quadcopter in the pursuit-evasion (PE) differential game. In this application, the pursuer learns its control strategies by interacting with evader and learning from past experiences. Both the critique and the actor are fuzzy inference systems (FIS). It is assumed that the pursuer knows only the instantaneous position and speed of the evader and vice versa. The FACL will generate the desired trajectory as the input for low-level controllers. Simulation results are presented for the PE differential game to demonstrate the practicality of our machine-learning algorithm
Energy Management for Coordinated Energy Hubs: From a Transaction Perspective

Integration of different energy carriers as well as converters, renewable energy, and energy storages provides energy hubs (EHs) with more flexibility. In this paper, a distributed energy management framework based on a peer to peer (P2P) transaction platform for the coordination of multiple EHs is developed. Each EH system determines its optimal operation strategy (i.e., energy conversion, storage, transaction) through distributed information-exchange with its network connected EHs, aiming to improve its individual economic performance. Specifically, a generic mathematic structure to generate the energy output prices is specified for each EH, which is further integrated into the multi-EH consensus-reaching process, where the energy prices adaptively change on the basis of the real-time operation state of each EH and the supply-demand condition of the local P2P market through the iterations, instead of performing as auxiliary variables wildly introduced in the traditionally distributed algorithms for the realization of the ultimate convergence. A three EH connected system is simulated and the case study validates the effectiveness of the proposed approach.
Highly sensitive fiber-enhanced Raman spectroscopy for simultaneous detection of SF6 decomposition

As the insulating medium, sulfur hexafluoride (SF6) is widely applied in gas insulation equipment. Over longterm operation, the solid insulation of the equipment is damaged by PD, causing it to react with SF6 to generate COS and CF4 which are critical SF6decomposition products inside the equipment. The detection of these two SF6 decomposition products can help in assessment of the type and severity of insulation defects. Compared to the absorption spectroscopy and photoacoustic spectroscopy, FERS can using a single wavelength laser to simultaneously achieve the high sensitivity detection of mixture gases. In this paper, a highly sensitive SF6 decomposition based on fiber-enhanced Raman spectroscopy is built up. The Raman spectroscopy of COS and CF4 was acquired. The characteristic peak of COS at 859 cm−1 and that of CF4 at 909 cm−1 were observed respectively. We study the relationship between laser power, pressure of fiber core and Raman scattering intensity, experimentally. The Raman scattering intensity is linear related to laser power and pressure. The response time of FERS which includes the filling time and evacuating time. was also examined. The filling time of the fiber is defined as the point when the intensity of the Raman peak does not change. FERS is promising to achieve the online monitoring technique to detect the SF6 decomposition components with highly selective and sensitive.
Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm

A significant cause of morbidity in COVID-19 infected patients admitted to the hospital is a severe dysregulated inflammatory response characterized as a cytokine storm, a key component of acute respiratory distress syndrome (ARDS). Here we have assembled a basic immune regulatory model from a list of 19 immune mediators with reported involvement in cytokine storm. Automated text-mining of over 2,500 full text journal publications using the MedScan natural language processing (NLP) engine identified 112 documented regulatory interactions coordinating the dynamic response of this network. This same text mining highlighted reported bi-directional associations between Coronavirus infection and a broad set of immune mediators producing a complex feedback pattern of host-pathogen interaction. Decisional logic parameters supporting the network’s dynamic response were identified such that observed responses to SARS-CoV infection in an in vitro system of Calu3 human lung adenocarcinoma cells could be accurately predicted. Of the 19 competing models, 2 supported a dominant inactive immune resting state, with a predicted onset of cytokine storm in 63% and 26% of simulated infections respectively. Discrete event simulation based on the latter suggest that some repurposing strategies might outperform popular use of hydroxychloroquine as a companion to anti-viral therapy.
A new method on lncRNA-disease-miRNA tripartite graph to predict lncRNA-disease associations

Finding the potential functions of lncRNAs is really vital for further study of human complex diseases. It requires a long time and other resources to uncover the potential lncRNA-disease associations by biological experiments, so developing computational methods to predict lncRNA-disease associations has become a hot topic in recent years. The prediction methods can basically rely on known lncRNA-disease associations or multitypes of data and molecular interaction networks. In this paper, we employ a method based on known lncRNA-disease associations, known disease-miRNA associations and validated lncRNA-miRNA interactions to construct a lncRNA-disease-miRNA tripartite graph and apply a modified resource allocation process to predict lncRNA-disease associations. In comparing with other related methods, our method achieves better performance with AUC and AUPR values of 0.984 and 0.828, respectively. Additionally, our method can predict latent lncRNA-disease associations for isolated lncRNAs or diseases.
Isolation of Solid Solution Phases in Size‐Controlled LixFePO4 at Room Temperature

State‐of‐the‐art LiFePO4 technology has now opened the door for lithium ion batteries to take their place in large‐scale applications such as plug‐in hybrid vehicles. A high level of safety, significant cost reduction, and huge power generation are on the verge of being guaranteed for the most advanced energy storage system. The room‐temperature phase diagram is essential to understand the facile electrode reaction of LixFePO4 (0 < x < 1), but it has not been fully understood. Here, intermediate solid solution phases close to x = 0 and x = 1 have been isolated at room temperature. Size‐dependent modification of the phase diagram, as well as the systematic variation of lattice parameters inside the solid‐solution compositional domain closely related to the electrochemical redox potential, are demonstrated. These experimental results reveal that the excess capacity that has been observed above and below the two‐phase equilibrium potential is largely due to the bulk solid solution, and thus support the size‐dependent miscibility gap model.
Influence of the Support of CoMo Sulfide Catalysts and of the Addition of Potassium and Platinum on the Catalytic Performances for the Hydrodeoxygenation of Carbonyl, Carboxyl, and Guaiacol-Type Molecules

The present work corresponds to part of a program aimed at upgrading oil obtained by pyrolysis of biomass by hydrotreatment (hydrodeoxygenation HDO). CoMo sulfide catalysts, nonsupported, supported on different supports (alumina, carbon, silica), or modified by K or Pt, were used. We used a model reacting mixture containing compounds representative of the molecules that must react to permit a primary stabilisation of the pyrolytic oil: 4-methylacetophenone (4-MA), diethylsebacate (DES), and guaiacol (GUA). In the reaction of the carbonyl group of the 4-MA it is shown that no important role is played by any acid-base mechanism; dispersion determines the activity. Acidity of the support influences the formation of active sites for decarboxylation and hydrogenation of the carboxyl group of DES. It was confirmed that guaiacol-type molecules lead to coking reactions. The role of acidity in the mechanism of these reactions is confirmed, but the modifications made in the catalysts in this work are still not sufficient to control coke deposition. The catalysts supported on carbon lead to the direct elimination of the methoxyl group of the guaiacol. Carbon, on the whole, seems to be a promising support. This work suggests that appropriate modifications of the hydrotreating catalysts can lead to a more effective process for stabilisation of the bio-oils by reaction with hydrogen. (C) 1995 Academic Press, Inc.