Miracle-dz/UniFAR-FTUs huggingface dataset:
Invasion patterns of non-native ants in natural ecosystems in warm, temperate New Zealand

Non-native species have the ability to negatively impact ecosystems, and the recipient biodiversity they may invade. However, they must first go through a series of abiotic and biotic filters that limit their ability to spread once established, which ultimately influences their distribution across different habitats. By understanding which habitats are most vulnerable to invasion, pest managers can prioritise their surveillance areas to focus on those most at risk. We assessed temporal and spatial invasion by ants within forested and non-forested ecosystems using a replicated sample design based on data collected in 2005 and by repeating the sampling 10 years later. We predicted that forested ecosystems would be more resistant than non-forested ecosystems to invasion, likely due to unfavourable habitat factors that limit ant establishment and spread, even after a 10-year period. In both sampling years, non-forested ecosystems were more susceptible to invasion by non-native ants, with ants being found at both the edge and in the interior of these habitats. Conversely, in forested ecosystems, non-native ants were concentrated on the forest edges. Although there was invasion of non-native ant species further into some non-forested ecosystems over the 10-year period, spread did not penetrate beyond the edge in forest ecosystems suggesting that habitat characteristics are important in determining non-native ant spread. We recommend prioritising surveillance and management of pest ants in non-forested ecosystems rather than forested ecosystems in New Zealand. Our findings highlight the need for future work on the mechanisms of spread of non-native ants in non-forested ecosystems.
Thermal Analyses of Loose Agave, Wheat Straw Fibers and Agave/Wheat Straw as New Hybrid Thermal Insulating Materials for Buildings

ABSTRACT Agave (AF) and wheat straw (SF) fibers are good raw materials for thermal insulation. In this study, loose agave or wheat straw fibers and hybrid boards of agave/wheat straw with different compositions and densities are tested for their thermal insulation qualities. Three new novel hybrid specimens are considered. In all specimens, cornstarch (CS) was used as a binder for the fibers. Thermal conductivity coefficient is obtained for each specimen in the temperature range 10–60°C with the resulting average value in the range 0.04555–0.06835 W/m K. Without binding, loose fibers of agave and wheat straws have an average thermal conductivity of 0.043592 and 0.044678 W/m.K, respectively, at the same range of temperature. Micrographs of the wheat straw fibers (SF) are conducted using the scanning electron microscope (SEM) which showed an average outer diameter between 1.28 and 1.98 mm and an average diameter of the bound porous tubes (multicellular) within the straw in the range 34.5 to 75.6 μm. Fourier transformation infrared (FT-IR) analyses of the wheat straw fiber are carried out which indicated stretching wavenumbers which present different chemical functional groups. Thermogravimetric analysis of the wheat straw fiber (TGA and DTGA) showed that the straws lose less than 3% of its mass at temperatures as high as 184°. Finally, the differential scanning calorimetry (DSC) analysis test of the wheat straw fiber indicates that endothermic transition starts at 342°C with a peak value at 749°C. Three-point bending moment tests for bound specimens are made and show an increase in both flexural stress and flexural modulus of the hybrid specimens. Using these hybrid specimens, agro, renewable and environmentally friendly materials in buildings will share in energy saving when used as insulation materials for building walls.
Securing the Human: A Review of Literature on Broadening Diversity in Cybersecurity Education

Recent global demand for cybersecurity professionals is promising, with the U.S. job growth rate at 28%, three times the national average [1]. In a global survey, 2,300 security managers reported that 59% of their security positions were unfilled, although 82% anticipated cyberattacks to their systems [2]. At the same time, the cybersecurity field is broadening, not only in technical concepts but also in human factors, business processes, and international law. The field has not become culturally diversified, however. Professionals hired in 2018 included only 24.9% women, 12.3% African Americans, and 6.8% Latinos [3]. These realities create an opportunity for higher education: diversify the profession while increasing the numbers of skilled computer scientists. New and integrated methods of attracting student populations in the field of cybersecurity are needed. This working group report analyzes the outcomes and approaches used in higher education to diversify the cybersecurity field through a review of the literature, identification of gaps, and recommendations for cybersecurity education researchers and practitioners.
A short note on analysis and application of a stochastic open-ended logistic growth model

ABSTRACT A minimal stochastic generalization of a deterministic open-ended logistic growth model is proposed for efficiently describing the biological growth of individual organisms under natural environment. The model is a system of stochastic differential equations. Its unique solvability in a strong sense is proven, and the behaviour of the solution is analysed. The presented model is then applied to the migratory fish Plecoglossus altivelis altivelis (P. altivelis, Ayu) having a one-year life history based on the data sets collected in 2017 and 2018.
Novel sensor to investigate microstructural contributions to corrosion of high‐palladium dental alloys

While the current trend is for dental crown restorations to be fabricated from esthetic ceramic materials such as lithium disilicate and zirconia, there is still widespread usage of metallic materials for porcelain-fused-to-metal restorations. The high-palladium casting alloys, which contain over 75% (wt%) Pd with Ga as the secondary element, are often selected for PFM restorations because of their favourable cost compared to alternative gold alloys, excellent mechanical properties and good bond strength to porcelain (Brantley & Laub, 2016). These alloys are classified into the two groups of Pd– Cu–Ga and Pd–Ga, and they contain secondary elements such tin and indium which modify mechanical properties and enhance porcelain bond strength (Brantley, Cai, Carr, & Mitchell, 1993; Carr & Brantley, 1991). High-Pd alloys contain multiple microstructural phases and generally contain ruthenium as a grain-refining element, which yields fine grain cast microstructures (Brantley et al., 1993; Carr & Brantley, 1991; Vermilyea, Cai, Brantley, & Mitchell, 1996). The matrix is a Pd-rich solid solution, and secondary precipitates appear along the grain boundaries and/or inside the grains. In the absence of a grain-refining element, the high-Pd alloy has a less desirable dendritic cast microstructure (Carr & Brantley, 1991), which can lead to the formation of “hot tears” in thin sections Received: 15 October 2019 | Revised: 9 December 2019 | Accepted: 21 December 2019 DOI: 10.1002/mds3.10060
Event-Triggered Prescribed Performance Control for a Class of Unknown Nonlinear Systems

This article is concerned with the tracking control problem for a sort of networked system with unknown nonlinear functions, unmatched disturbances, and event-triggered input. A novel prescribed performance control strategy together with an actuator update protocol is developed to form a solution. Different from the existing results, only a binary signal (either 0 or 1) needs to be sent to the actuator. In this way, the number and the bit of data transmission are both curtailed, leading to the economy of the communication cost. On the other hand, output tracking with guaranteed transient and steady-state performance is achieved regardless of unknown nonlinearities, disturbances, and measurement errors. Finally, simulation results are given to illustrate the established theoretical findings.
Modeling and Economic Optimization of the Membrane Module for Ultrafiltration of Protein Solution Using a Genetic Algorithm

The performance of cross-flow ultrafiltration is greatly influenced by permeate flux behavior, which depends on many factors, including solution properties, membrane characteristics, and operating conditions. Currently, most research focuses on improving membrane performance, both in terms of permeability and selectivity. Only a few studies have paid attention to how the membrane module is configured and operated. In this study, the geometric design and operating conditions of a membrane module are considered as multivariable optimization variables. The objective function is the annual cost. The cost consists of a capital investment depending on the plant scale and an operating expense associated with energy consumption. In the optimization problem, the channel dimensions (width × length × height), and operating conditions (the inlet pressure and recirculation flow rate) were considered as decision variables. The operating configuration of the membrane plant is assumed to be feed and bleed mode, and a model including the pressure drop is introduced. The model is used to simulate the membrane plant and calculate the membrane area and energy usage, which are directly related to the total cost. The genetic algorithm is used for the optimization. The effect of individual parameters on the total cost is discussed.
Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis

Deep networks based mechanical intelligent diagnosis has been recently attracting considerable attentions with the development of Industry 4.0. Unfortunately, a more practical diagnostic scenario, i.e., unsupervised partial transfer diagnosis, has not yet been well addressed. In view of this, a novel unsupervised intelligent diagnosis framework named classifier inconsistency-based domain adaptation network is proposed in this article. In this approach, two discriminative one-dimensional convolutional networks are designed as the basic architecture. The source samples of the same categories as the target domain are then identified and emphasized to boost positive network training. Meanwhile, the classifier inconsistency is introduced to guide the model to learn discriminative and domain-invariant representations for the correct classification of unlabeled target data. Extensive experiments on two datasets are conducted to evaluate the proposed method. Additionally, five popular methods are selected for comparison. The comprehensive results validate the effectiveness and superiority of the proposed approach.
The Role Of School Leadership In Teacher Retention In Title 1 Schools

The aim of this qualitative study was to examine the ways in which teachers’ perceptions of school leadership, particularly principals, impact the attrition and the retention of highly qualified educators. The focus of this study was the analysis of teachers’ views of the various philosophies of leadership and how the practices which stem from those philosophies affect the efficacy of the teachers. Because of the correlation between teacher retention and leadership styles, the behavioral leadership model of principals developed by Urick and Bowers (2014) was used as a conceptual framework for the study. The findings of this research indicated that while the interactions with school leaders affect the morale and, in some cases, the stress levels of the teachers, the participants are intrinsically motivated to remain in Title 1 schools. Such motivators included family dynamics, senses of community, and sociological ideologies. While most of the leader-member exchanges were positive and of high quality, there is a need for teacher-driven, differentiated professional development such as flexible meetings through online platforms and apps so that more time could be allotted for collaborative and individual planning. Even though the state evaluation rubric encompasses ten performance standards, the teachers who participated in this study wanted a more detailed feedback system that allows consistent, ongoing dialogue between the leaders and the teachers. Consistent, quality leader-member exchanges are needed to help to build levels of trust and mutual understanding.
Scaled Nested Key Equation Solver for Generalized Integrated Interleaved Decoder

Generalized integrated interleaved (GII) codes nest short Reed-Solomon or BCH sub-codewords to form codewords of higher error-correction capability. They enable hyper-speed decoding and efficient failure recovery in distributed storage. Although the nested decoding can continue from the key equation solver (KES) result of the sub-codeword decoding, its discrepancy coefficient computation leads to long critical path. A recent nested KES architecture reduced the critical path to the same as that of the KES for sub-codeword decoding. Nevertheless, the nested KES has large area. This brief proposes a novel scaled nested KES algorithm and architecture to reduce the area requirement. Scaled versions of the polynomials are utilized to enable the sharing of product terms and the nested KES algorithm is reformulated to efficiently incorporate the scaling without changing the decoding output. As a result, the number of expensive finite field multipliers is significantly reduced. For an example code over $GF(2^{8})$ that corrects up to 28 errors, from architectural analysis, the proposed architecture achieves 17% area reduction with only one more gate in the critical path.
Speech Emotion Recognition Systems: Review

: In this paper, survey of different types of speech emotions recognition system are analyzed. In human machine interface application, emotion recognition from the speech signal has been research topic since many years. The classifiers are used to differentiate emotions such as anger, disgust, fear, joy or happiness, sadness, surprise, neutral state, etc. In speech, domain’s important factor is to understand the speech. But automated speech detection is a big challenge which is solved by extracting best features of signals (speech). In Emotion Detection, domain has two types of features i.e. Important Utterance and Prosodic features. Finally, in respect of performance speech emotion recognition systems are discussed in the last section of this survey. This section also provides the possible ways of enhancing performance of Speech Emotion Recognition Systems (S.E.R.).
Characterizing and Assessing Temporal Heterogeneity: Introducing a Change Point Framework, with Applications on the Study of Democratization

Abstract Various theories in political science point to temporal heterogeneity in relationships of interest. Yet, empirical research typically ignores such heterogeneity or employs fairly crude measures to evaluate it. Advances in models for change point detection offer opportunities to study temporal heterogeneity more carefully. We customize a recent such method for political science purposes, for instance so that it accommodates panel data, and provide an accompanying R-package. We evaluate the methodology, and how it behaves when different assumptions about the number and abrupt nature of change points are violated, by using simulated data. Importantly, the methodology allows us to evaluate changes to different quantities of interest (for various estimators). It also allows us to provide comprehensive estimates concerning uncertainty in the timing and size of changes. We illustrate the utility of this flexible change point methodology on two types of regression models (Probit and OLS) in two empirical applications. We first re-investigate the proposition by Albertus (2017) that labor-dependent agriculture had a more pronounced negative effect on democratic survival before the “third wave of democratization. ” Next, we utilize data extending from the French revolution to the present, from V-Dem, to examine the time-variant nature of the income–democracy relationship.
Analysis of Progressive Duplicate Data Detection

Duplicate detection is the process of identifying multiple representations of same real world entities. Today, duplicate detection methods need to process ever larger datasets in ever shorter time: maintaining the quality of a dataset becomes increasingly diﬃcult. We present two novel, progressive duplicate detection algorithms that signiﬁcantly increase the eﬃciency of ﬁnding duplicates if the execution time is limited, They maximize the gain of the overall process within the time available by reporting most results much earlier than traditional approaches. Comprehensive experiments show that our progressive algorithms can double the eﬃciency over time of traditional duplicate detection and signiﬁcantly improve upon related work.
Attitudes of the parents towards the science lesson reliability-validity study

It is important to determine the attitude which is an indicator of emotions for a lesson. In particular, the development of the scale to be used to determine the attitude of the parents towards the science lesson will contribute to the field. The aim of this study is to develop a scale for use in determining attitudes of the parents towards the science lesson. The scales were applied to 389 parents of the 6th, 7th and 8th grades students. SPSS and LISREL programs were used to analyze the data. The reliability values and exploratory factor analyzes of the scales were calculated with the help of SPSS program. Cronbach Alpha value, Kaiser-Meyer-Olkin and Bartlett tests were performed at this stage. Afterwards, confirmatory factor analysis was performed with the help of LISREL program and compliance indices of the scales were determined. Then, independent sample T-Test and one-way ANOVA tests were used to answer the research questions. As a result of analyzes, the reliability and exploratory factor analysis of the scale were all acceptable. In addition, the confirmatory factor analysis pattern table values are acceptable but some of the compliance indices are below the acceptable values. As a result, a scale was developed to determine the attitudes of the parents towards the science lesson. According to the average of the attitude scores, it was discovered that the attitudes of the parents towards the science lesson were at a good level. In addition, there was no relationship between the demographic structure of parents and their attitudes towards the science lesson.
Comprehensive benchmarking of computational deconvolution of transcriptomics data

Many computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking. Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference. Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.
Application of webometrics methods for analysis and enhancement of academic site structure based on page value criterion

This paper describes a formalized procedure for exploring a site using webometrics methods. The procedure involves gathering details on a site’s structure, constructing and exploring the resulting webgraph, deﬁning the correctness criterion, identifying control actions that would improve the structure under the given criterion, testing the correctness criterion on real-world examples and developing recommendations on improving the structure. PageRank is used as a criterion to evaluate the value of web pages. The value is determined by the presence/absence of a link pointing to that page from the homepage of the site. Going by the correctness criterion, valuable pages of a site should have the highest PageRank among all other pages of that site. Control action consists of removing non-valuable directories (and transforming them into independent sites), whose root page has a high PageRank. Experiments are conducted on three faculty sites of major universities in USA, Russia and Nigeria. The approach is shown to be applicable and reasonable in all cases.
Characterization of the complete chloroplast genome of Allium przewalskianum (Amaryllidaceae)

Abstract Allium przewalskianum (Amaryllidaceae) is an important vegetable and/or condiment for Tibetans, Indians, and Nepalese in the highlands of the Himalayas. Here, we reported the complete chloroplast genome of A. przewalskianum using the next-generation sequencing method. The size of the chloroplast genome is 153,509 bp in length, including a large single copy region (LSC) of 82,302 bp, a small single copy region (SSC) of 17,717 bp, and a pair of inverted repeat (IR) regions with 26,745 bp. The A. przewalskianum chloroplast genome encodes 114 genes, including 68 protein-coding genes, 38 tRNA genes, and eight rRNA genes. Phylogenetic tree analysis suggested that A. przewalskianum was closely related to A.ampeloprasum and A.sativum.
A methodology to assess the impact of end-user centric innovations on railway transportation attractiveness

The European Shift2Rail initiative has the target to improve the European railway system and to increase the use of it by passengers. The impact of the technical improvement developed by Shift2Rail is evaluated by Key Performance Indicators (KPIs). The relevant technical key targets are improved capacity, reliability and life-cycle-cost (LCC). But to increase the use of the railways there are also activities within Shift2Rail, which target to improve directly the attractiveness of the railways and focus on customer demand. Especially the innovation program 4 (IP4) “IT Solutions for Attractive Railway Services” aims to support customer satisfaction by IT solutions. During development of the approach for the evaluation of the three technological KPIs capacity, reliability and LCC it was identified that they are not suitable to evaluate innovations directly focussing on the demand of the railway system. A further approach was implemented to evaluate the impact of the technological developments in the other IPs as well as the direct impact of the IT solutions developed in IP4 on the attractiveness of the rail system. The basic concept of this approach is to identify major issues which are the subject of “Areas of Major Possible Improvement (AMPI)”, which means that issues are identified and categorised, which are reported by users as negative issues in the today’s system. Those can be solved by technical solutions on the trains, signalling or infrastructure or by IT-Solutions addressing directly the passenger. The main improvement of the approach presented here, is to combine the impact of both in one integrated model.