Miracle-dz/UniFAR-FTUs huggingface dataset:
Spire: A Cooperative, Phase-Symmetric Solution to Distributed Consensus

All existing solutions to distributed consensus are organised around a Paxos-like structure wherein processes contend for exclusive leadership in one phase, and then either use their dominant position to propose a value in the next phase or elect an alternate leader. This approach may be characterised as adversarial and phase-asymmetric, requiring distinct message schemas and process behaviours for each phase. In over three decades of research, no algorithm has diverged from this basic model, alluding to it perhaps being the only viable solution to consensus. This paper presents a new consensus algorithm named Spire, characterised by a phase-symmetric, cooperative structure. Processes do not contend for leadership; instead, they collude to iteratively establish a dominant value and may do so concurrently without conflicting. Each successive iteration is structured identically to the previous, employing the same messages and invoking the same behaviour. By these characteristics, Spire buckles the trend in protocol design, proving that at least two disjoint cardinal solutions to consensus exist. The resulting phase symmetry halves the number of distinct messages and behaviours, offering a clear intuition and an approachable foundation for learning consensus and building practical systems.
Measuring financial performance of Indian manufacturing firms: application of decision tree algorithms

Purpose Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance. Design/methodology/approach The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures. Findings Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm. Originality/value To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.
Molecular population genetics of Sex-lethal (Sxl) in the Drosophila melanogaster species group: a locus that genetically interacts with Wolbachia pipientis in Drosophila melanogaster

Abstract Sex-lethal (Sxl) is the sex determination switch in Drosophila, and also plays a critical role in germ-line stem cell daughter differentiation in Drosophila melanogaster. Three female-sterile alleles at Sxl in D. melanogaster were previously shown to genetically interact to varying degrees with the maternally inherited endosymbiont Wolbachia pipientis. Given this genetic interaction and W. pipientis’ ability to manipulate reproduction in Drosophila, we carried out a careful study of both the population genetics (within four Drosophila species) and molecular evolutionary analysis (across 20 Drosophila species) of Sxl. Consistent with earlier studies, we find that selective constraint has played a prominent role in Sxl’s molecular evolution within Drosophila, but we also observe patterns that suggest both episodic bursts of protein evolution and recent positive selection at Sxl. The episodic nature of Sxl’s protein evolution is discussed in light of its genetic interaction with W. pipientis.
A product yield study of the reaction of HO2 radicals with ethyl peroxy (C2H5O2), acetyl peroxy (CH3C(O)O2), and acetonyl peroxy (CH3C(O)CH2O2) radicals

The reaction between HO2 and RO2 radicals represents an important chemical sink for HOx radicals in the atmosphere. On the basis of a few product yield studies, these reactions are believed to form hydroperoxides almost exclusively (R8a), although several different reaction channels may be thermodynamically accessible (R8a−d): RO2 + HO2 → ROOH + O2 (R8a); RO2 + HO2 → ROH + O3 (R8b); RO2 + HO2 → RO + OH + O2 (R8c); and RO2 + HO2 → R‘CHO + H2O + O2 (R8d). Branching ratios for reaction R8 were measured for three organic peroxy radicals: ethyl peroxy (C2H5O2), acetyl peroxy (CH3C(O)O2), and acetonyl peroxy (CH3C(O)CH2O2) radicals. Product yields were measured using a combination of long-path Fourier transform infrared spectroscopy and high-performance liquid chromatography with fluorescence detection. Measured branching ratios for the reaction of the three organic peroxy radicals with HO2 are as follows: ethyl peroxy, Y8a-ETH > 0.93 ± 0.10, Y8b-ETH = Y8c-ETH = Y8d-ETH = 0; acetyl peroxy, Y8a-ACT = 0.40 ± 0.
A Framework for Industry 4.0 Readiness and Maturity of Smart Manufacturing Enterprises: A Case Study

Recently, researchers have proposed various maturity models (MMs) for assessing Industry 4.0 (I4.0) adoption; however, few have proposed a readiness framework (F/W) integrated with technology forecasting (TF) to evaluate the growth of I4.0 adoption and consequently provide a roadmap for the implementation of I4.0 for smart manufacturing enterprises. The aims of this study were (1) to review the research related to existing I4.0 MMs and F/Ws; (2) to propose a modular MM with four dimensions, five levels, 60 second-level dimensions, and 246 sub-dimensions, and a generic F/W with four layers and seven hierarchy levels; and (3) to conduct a survey-based case study of an automobile parts manufacturing enterprise by applying the MM and F/W to assess the I4.0 adoption level and TF model to anticipate the growth of I4.0. MM and F/W integrated with TF provides insight into the current situation and growth of the enterprise regarding I4.0 adoption, by identifying the gap areas, and provide a foundation for I4.0 integration. Case study findings show that the enterprise’s overall maturity score is 2.73 out of 5.00, and the forecasted year of full integration of I4.0 is between 2031 and 2034 depending upon the policy decisions.
Analyzing the Coordination Relationship between Urban Built Environment and Traffic Level

The coordination relationship between urban built environment and transport system is an indispensable field in the study of urban planning. Recent research efforts in built environment and transport system have focused on the effects of built environment on travel behaviors, such as car ownership, choice of travel mode, and travel frequency. These travel behaviors will affect the traffic level. However, research studies on direct assessments of links between built environment and traffic level are still limited. This paper aims to fill this gap by modeling with data envelopment analysis based on Point of Interest (POI) data and floating car data collected in Jinan, China. It is found that the coordination relationship between built environment and traffic level is poor in Jinan. With regard to the built environment input index, the distance from the city center has the greatest influence on the coordination relationship. And for the built environment output index, bus stop influences the coordination relationship most significantly. This research can support the provision of quantitative basis for the formulation of governance priorities for traffic governance policies.
Ligand Effects on the Structure and the Electronic Optical Properties of Anionic Au25(SR)18 Clusters

This study addresses how ligands module the structure and the electronic optical properties of a large set of the experimentally known anionic thiolate-protected gold clusters, Au25(SR)18[1−]. Starting from the experimental crystal structure, computational density functional theory calculations reveal that low-polarity R groups do not disturb the Au25S18 framework significantly, such that the inversion symmetry(Ci) of the crystalline state is retained. In the case of p-thiolphenolate ligands, p-SPhX, a major distortion of the Au25S18 framework, destroys the inversion symmetry, the distortion increasing in the order given X = H, Cl, NO2 and CO2H. For branched R groups, linking −CH3 or −NH2 groups at the two-position of the phenylethylthiolate ligand, the inversion symmetry is retained and lost, respectively; similarly, the N-acetyl-cysteine ligand also distorts the framework. These results demonstrate a systematic preference of inversion-symmetric versus nonsymmetric framework depending on the ligand-type.
The structure of zeolite Li-A(BW) determined from single crystal data

An X-ray diffraction study of a single crystal of Li—A(BW) zeolite (LÌAIS1O4, H 2 0, a = 10.313(1), b = 8.194(1), c = 4.993(1) Â; Pnali, Z = 4, Dc = 2.27gcm~) used 619 diffraction intensities (CAD-4 diffractometer), ΜοΑΓα radiation (ΑΜοΑΓα = 0.71069 Â), and was refined to R (unweighted) = 0.051. The structure contains Si04 tetrahedra (mean S i O distance 1.627 Â), A104 tetrahedra (mean A l O distance 1.743 Â) and L1O4 tetrahedra (three framework oxygen atoms and one water oxygen atom, mean Li—O distance 1.97 Â). The water molecule forms two hydrogen bonds (one to another water molecule (Ο · · · O distance 2.911 Â) and one to a framework oxygen atom (O O distance 3.003 Â).
Robust Nonlinear Controller Design for DC–AC Converter in Grid-Connected Fuel Cell System

This article presents a robust nonlinear controller for distributed fuel cell in the three-phase grid-connected system. A partial feedback linearization scheme is applied to linearize the grid-connected fuel cell system. The mismatches between the actual system and the dynamical model are considered uncertainties. The noise decoupling capability of the feedback linearized control scheme is also demonstrated. A loop-shaping-based $\text{H}_\infty$ mixed-sensitivity controller is designed for the partially linearized plant of the grid-connected fuel cell system. Performance of the designed controller is investigated under power factor changes, three-phase short-circuit fault, parameter variation, and measurement noise in a hardware-in-loop simulation platform with OP 4510.
Socio-Economic Drivers for Farming Households’ Decision to Conserve Nature around Mount Uluguru, Tanzania

Nature conservation means preservation or restoration of the environment and wildlife. The activity is just beyond someone's will; it is highly influenced by socio-economic factors. This study examined the drivers for farming households to conserve the nature around Mount Uluguru in Morogoro, Tanzania. A survey was conducted in five villages around the Arc Uluguru Mountain, and only 106 respondents were randomly selected. To supplement the information, focus group discussions were held with village leaders and environmental committee members in each village who gave their precious information for analysis. Descriptive analysis was done using frequencies, percentage, and mean for examining characteristics of the sampled population, while the binary logistic model was used to analyze the factors that drive farmers to participate in nature conservation. It was found that farming experience, access to support services and awareness in bylaws and regulations increase the chances of participating in environmental conservation amongst the household members. Also, being a male individual and having old age decreases the chances of engagement to nature conservation activities. The study recommends that, in conservation activities, gender roles should be taken into consideration, as anyone can take charge and participate in nature conservation. Furthermore, more training, materials, and equipment are needed from governmental and NGO’s to help nature conservation in Mount Uluguru.
A Dataset of COVID-Related Misinformation Videos and their Spread on Social Media

This dataset contains metadata about all COVID-related YouTube videos which circulated on public social media, but which YouTube eventually removed because they contained false information. It describes 8,122 videos that were shared between November 2019 and June 2020. The dataset contains unique identifiers for the videos and social media accounts that shared the videos, statistics on social media engagement and metadata such as video titles and view counts where they were recoverable. The dataset has reuse potential for research studying narratives related to the coronavirus, the impact of social media on knowledge about health and the politics of social media platforms.
Public key cryptography based on Moufang loops

Abstract One of the earliest examples of public key cryptography is the Diffie-Hellman key exchange protocol, which is based on the discrete logarithm problem. In 2000, Ko, Lee et al. modified the Diffie-Hellman protocol by using braid groups as platform where the security of the protocol lies on the conjugacy search problem. In this paper, we propose two key exchange protocols that use two well known classes of nonassociative Moufang loops as potential platforms.
Pre-service teacher research: a way to future-proof teachers?

ABSTRACT Internationally, the attention to pre-service teacher research and inquiry in teacher education is growing. In the Netherlands, pre-service teacher research has been a compulsory component of primary teacher education for a decade. The assumption is that such research can help ‘future-proof’ teachers. This study examines the relationships among the quality of inquiry, the quality of teaching and pre-service teachers’ perception of this research. Scores for assessments of graduating pre-service teachers (N=650) and a survey (n=236) were used as measurements. The findings indicate positive perceptions of practitioner research and a positive correlation between the quality of inquiry and quality of teaching. Using these data, the study identifies four profiles of pre-service teachers, differentiated by their perceived learning outcomes.
Investigating the Impact of Cracks on Solar Cells Performance: Analysis Based on Nonuniform and Uniform Crack Distributions

The article investigates the detrimental effect of nonuniform and uniform crack distributions over a solar cell in terms of open-circuit voltage (<inline-formula><tex-math notation="LaTeX">${{\bm{V}}_{{{\bf oc}}}}$</tex-math></inline-formula>), short-circuit current density (<inline-formula><tex-math notation="LaTeX">${{\bm{J}}_{{{\bf sc}}}}$</tex-math></inline-formula>), and output power, the latter under a wide range of irradiance conditions. The experimental procedure to detect the cracks relies on electroluminescence (EL) imaging, which is nondestructive and requires a relatively low amount of time. The Griddler software is adopted to translate the EL-taken image into <inline-formula><tex-math notation="LaTeX">${{\bm{V}}_{{{\bf oc}}}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${{\bm{J}}_{{{\bf sc}}}}$</tex-math></inline-formula> maps. The main findings can be summarized as follows: the nonuniformly and uniformly cracked cells are both jeopardized in terms of output power; the loss corresponding to the cell with nonuniform distribution of cracks is increasingly higher than the uniformly cracked counterpart as the irradiance hitting the cells grows; and all cells affected by nonuniform cracks are severely damaged in terms of fingers and rear busbar, which concur to limit the maximum output current.
Sound Localization for Ad-Hoc Microphone Arrays

Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Previously, sound source has been localized using six or more microphones. The precision of sound localization has been demonstrated to increase with the use of more microphones. In this research, however, we minimized the use of microphones to reduce the complexity of the algorithm and the computation time as well. The method results in novelty in the field of sound source localization by using less resources and providing results that are at par with the more complex methods requiring more microphones and additional tools to locate the sound source. The average accuracy of the system is found to be 96.77% with an error factor of 3.8%.
EduChain: A highly available education consortium blockchain platform based on Hyperledger Fabric

With the problems of data sharing and information diddling in the field of education, we construct a highly available education consortium blockchain platform to ensure trusted sharing and privacy protection of education data. We employ erasure codes to process blockchain ledger files and optimize the data storage model according to the characteristics of education data, which can reduce the storage volume effectively. A HotStuff consensus algorithm is designed to access the ordering service of Hyperledger Fabric. A suitable educational blockchain network architecture based on the node complexity of education scenarios is proposed to achieve the high availability of the platform. To manage the education blockchain network, we implement the Fabric deployment based on Kubernetes and achieve the goal of including chaincode into Kubernetes environmental management. To improve the resource utilization of chaincode, we explore the new way of chaincode management by the functional computing service. Finally, on the premise of ensuring a 1/2 fault tolerance rate, the total ledger has decreased by 53.56%. Our platform enhanced the Byzantine fault tolerance while ensuring higher efficiency. Experimental results show that our platform is quite suitable for education scenario with many nodes.
Modeling and Optimizing the Effects of Insert Angles on Hard Turning Performance

To analyze hard turning performance characteristics, a new mathematical model was developed for the hard turning process, and cutting force (CF), another important response for cutting machining, was also studied in the present work. The analysis of the mathematical model and experimental results revealed that thrust force (Fy) was the largest, followed by tangential force (Fz) and feed force (Fx). The resultant CF was most influenced by inclination angle (IA) with 25.02%, followed by rake angle (RA) (14.26%) and cutting edge angle (CEA) (10.04%). Increasing CEA changed the position of cutting on the tool-nose radius and increased local negative RA and correspondingly local clearance angle (CA). Meanwhile, increasing negative RA and IA resulted in larger local negative RA and CA. Moreover, local RA and local CA were the main geometric factors affecting surface roughness (SR), tool wear (TW), and CF. Increasing local negative RA resulted in higher SR and CF. In contrast, increasing local CA resulted in lower SR, TW, and CF. Under specific conditions, the positive effects of the local CA overcame the negative effects of the local negative RA, leading to a simultaneous decrease in SR and TW. The proposed novel mathematical model can be further applied to calculate local CF, cutting temperature, and TW for each cutting-edge element, to analyze and optimize the hard turning process.
Python Packages for Exploratory Factor Analysis

ABSTRACT Exploratory Factor Analysis (EFA) is a widely used statistical technique for reducing data dimensionality and representing latent constructs via observed variables. Different software offer toolsets for performing this analysis. While Python’s statistical computing ecosystem is less developed than that of R, it is growing in popularity as a platform for data analysis and now offers several packages that perform EFA. This article reviews EFA modules in the statsmodels, FactorAnalyzer, and scikit-learn Python packages. These packages are discussed with regard to official documentation, features, and performance on an applied example.
Detecting Invalid Associations between Fare Machines and Metro Stations Using Smart Card Data

Data quality is essential for its authentic usage in analysis and applications. The large volume of automated collection data inevidently suffers from data quality issues including data missing and invalidity. This paper deals with an invalid data problem in the automated fare collection (AFC) database caused by the erroneous association between the fare machines and metro stations, e.g., a fare machine located at Station A is wrongly associated with Station B in the AFC database. It could lead to inappropriate fare charges in a distance-based fare system and cause analysis bias for planning/operation practice. We propose a tensor decomposition and isolation forest-based approach to detect and correct the invalid associated fare machines in the system. The tensor decomposition extracts features of passenger flows and travel times passing through fare machines. The isolation forest coupled with a neural network (NN) takes these features as inputs to detect the wrongly associated fare machines and infer the correct association stations. Case studies using data from a metro system show that the proposed detection approach achieves over 90% accuracy in detecting the invalid associations for up to 35% invalid associations. The inferred association has a 90% accuracy even when the invalid association ratio reaches 40%. The proposed data-driven invalid data detection method is useful for large-scale data management in terms of data quality check and fix.
Detection of Heavy Metals in Food and Agricultural Products by Surface-enhanced Raman Spectroscopy

ABSTRACT Heavy metals accumulating in the human body produce physiological toxicity by interfering with the transport of human proteins and enzymes. Heavy metals detection is significant for food safety assurance. This review focuses on recent advances of heavy metals detection of food and agricultural products by surface-enhanced Raman spectroscopy (SERS). The article covers the SERS basic principles and advances in heavy metals detection, including mercury, arsenic, cadmium, lead, chromium among others. Insights in the potential of combining chemometrics and multivariate analysis with SERS and the exploration of novel SERS substrate platforms from both macro and micro scale are discussed. Finally, future application of SERS in heavy metal detection are prospected. SERS is a powerful and promising technique offering the advantages of simple sampling, rapid data collection and non-invasiveness. The findings of this study can allow better understanding of the heavy metals’ occurrence and the possibility of its detection using SERS.