Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Lifetime Exponential-X Family of Distributions with Applications to Reliability Data

Modeling reliability data with nonmonotone hazards is a prominent research topic that is quite rich and still growing rapidly. Many studies have suggested introducing new families of distributions to modify the Weibull distribution to model the nonmonotone hazards. In the present study, we propose a new family of distributions called a new lifetime exponential-X family. A special submodel of the proposed family called a new lifetime exponential-Weibull distribution suitable for modeling reliability data with bathtub-shaped hazard rates is discussed. The maximum-likelihood estimators of the model parameters are obtained. A brief Monte Carlo simulation study is conducted to evaluate the performance of these estimators. For illustrative purposes, two real applications from reliability engineering with bathtub-shaped hazard functions are analyzed. The practical applications show that the proposed model provides better fits than the other nonnested models.
New Forest Code effects over smallholder’s intention to trade non-timber forest products

Abstract. Brites AD. 2020. New Forest Code effects over smallholder’s intention to trade non-timber forest products. Asian J For 21: 41-45. The Brazilian new Forest Code (NFC) allows the sustainable economic use of one of its main mechanisms for native vegetation protection: the Legal Reserves. Smallholders' intention to trade non-timber forest products (NTFP) from such areas was accessed. Data were collected through 350 in-person surveys in two municipalities from Bahia, Brazil. Outcomes showed that more than half of the respondents do not intend to trade NTFP from their Legal Reserve areas and, the lack of knowledge about the process to obtain government permission for the economic use of these areas, was the main reason gave to justify it. Further, higher household incomes showed a negative correlation with the intention. Incentives from public or private policies and dissemination about the steps needed to obtain permission for Legal Reserve’s sustainable use are needed for engaging smallholders in NTFP trade. With these incentives, it would be possible to increase the NFC potential for adding economic value to the protected native vegetation and for linking conservation with the economic development of rural areas inhabitants. Thus, these outcomes add up to previous findings of the benefits of NTFP trade, and in terms of practice and policy, they can help in the development of environmental policies that incentivize the NTFP trade, contributing to increase landholders’ incomes and to the NFC compliance.
A New Pooling Method For Improvement OfGeneralization Ability In Deep Convolutional Neural Networks

: As powerful visual models, deep learning models, in particular, deep convolutional neural networks(DCNNs) have demonstrated remarkable performance in various challenging artificial intelligence and machine learning tasks and attracted considerable interests in recent years. A pooling process plays a very important role in deep convolutional neural networks, which serves to reduce the dimensionality of processed data for decreasing computational cost as well as for avoiding overfitting and improving the generalization capability of the network. Although standard pooling techniques, such as the max pooling and the 𝑙 pooling (where 𝑝 ≥ 1 ) are typically adopted in various studies, we alternatively propose, in this paper, a new pooling method named 𝑙 pooling in order to improve the generalization capability of DCNNs. Experimental results on two image benchmarks indicate that 𝑙 pooling outperforms the existing pooling techniques in classification performance as well as is efficient for enhancing the generalization capability of DCNNs. Moreover, we show that the 𝑙 pooling combined with other regularization methods, such as dropout and batch normalization, is competitive with other existing strategies in classification performance.
Higher Education Qualification Evaluation

— Qualification developed by the requirements of labor market is more competitive than the other ones. In Hungary, this issue constitutes one of the central elements of the higher education reform taking place nowadays. In this paper, a system in progress is presented, which aims at evaluating the learning outcomes of Business Informatics Bachelor’s degree program at Corvinus University of Budapest versus the competences needed by the labor market, as appeared on a job recruitment portal. Ontology-based learning and matching domains are touched in the course of the development, so it is necessary to choose their appropriate tools to integrate them into a system. The tools written in Java constitute the base of the development.
AUTOMATIC STEEL MICROSTRUCTURAL QUANTIFICATION BY CONVOLUTIONAL NEURAL NETWORKS

The microstructural analysis of a material allows the complete characterization of its mechanical properties. Thus, the performance of a mechanical component depends heavily on the identiﬁcation and quantiﬁcation of its microstructural constituents. Currently, this process is still done mostly manually by experts, making it slow, very labor-intensive and inefﬁcient. It is estimated that an experienced expert takes 15 minutes per image to perform the proper identiﬁcation and quantiﬁcation of microconstituents. Therefore, a computational tool could greatly assist to improve the performance in this task. However, since a microstructure can be a combination of different phases or constituents with complex substructures, their automatic quantiﬁcation can be very hard and, as a result, there are few previous works dealing with this problem. Convolutional Neural Networks are promising for this type of application since recently this type of network has achieved great performance in complex applications of computational vision. In this work, we propose an automatic quantiﬁcation of microstructural constituents of low carbon steel via Convolutional Neural Networks. Our dataset consists of 210 micrographs of low carbon steel, and this amount of images was increased through data augmentation techniques, resulting in a total of 672 samples for training. With regard to network architectures, we used the AlexNet trained from scratch and the VGG19 and Xception both pre-trained. The results showed that CNNs can quantify microstructures very effectively
THE ABILITY OF BIOGAS AS SUSTAINABLE ENERGY TO MEET RURAL LIVELIHOODS

The increase in the environmental pollution and economic growth, energy usage requirement cause increase in world clean energy demand. Biomethanization is one of the promised technologies to produce clean and renewable energy source compared to fossil energy resources. This research was done in two trials, the first trial focused on comparing the potential and actual values for heat and electric energy from different biomass hence, select the best for the equal energy required for different livelihoods in rural applications. The second trial, the preferable feedstocks synthesis (25% cattle dung + 75% poultry droppings) from the first trial was select to apply in the second fermentation, which done in home scale digester (68 l) under three different agitations at under mesophilic temperature 38°C. The results showed that using a combination of feedstocks (co-digestion) led to improving the biogas yield by 67.68% - 7.10% because of the increase in organic content and optimizing the conditions for anaerobic digestion. Also, the percentage for every row feedstock in the combination has an equally significant aspect. The calorific efficiency was average 54 -78 % and correspondingly, the electricity produced efficiency ranged between 52.43 - 68.09 %. Hence, the combinations of feedstocks can be selected to be enough to purposes of heat and electricity applications. In the home scale digester, the regression equation for methane yield to retention time under two-time agitation per day was y = 0. 347x + 0.5016x2 -0.0162x3. In additional it was found that the biogas production was 3993.87 l/month from 25 kg only of animal wastes, this covered 4 days of cooking or gas lamp demands or 12 days of a refrigerator. Hence, decrease the using of conventional sources and improve the healthy living in rural communities.
The optimum sampling unit area on the spatial distribution pattern of grassland on the saline land in Linze

The spatial distribution of grassland community was studied on the saline land in Linze using different sampling areas and zoning methods.Results showed that the spatial distribution pattern of grassland community was sensitive to sampling sizes and zoning methods.The effect of aggregation was very obvious when the sampling area was under 12500m2.The different zoning methods had certain effects on the spatial distribution pattern under same sampling sizes.However,the effect of zoning methods on the spatial distribution pattern decreased as sampling area increased.Therefore,it is essential to optimise the sampling areas and select the appropriate zoning methods to minimise the difference between sampling units for the research on spatial distribution of grassland community on saline land.
NICOTIANA TABACUM AS A POTENTIAL PLATFORM FOR THE PRODUCTION OF RECOMBINANT ANTI-TOXOPLASMA SINGLE-CHAIN VARIABLE FRAGMENT (scFv) ANTIBODY

Abstract Plant systems have now gained much attention as they provide low risk of pathogen contamination and cost of production, appropriate post-translational modification, and multimeric assembly capability compared to the mammalian and bacterial hosts. In this study, we aimed to produce single-chain fragment antibody (scFv) encoding anti-Toxoplasma proteins, TP60 in Nicotiana tabacum cv. SR1. Leaf explants of N. tabacum were infected with Agrobacterium tumefaciens strain LBA4404 harbouring binary vector pCAMBIA1304 containing TP60 gene. Putative transformants were confirmed through GUS and GFP qualitative assays. Bands observed at the predicted size of 914 bp confirmed the presence of TP60 transgene and the transgene was stably integrated in both T0 and T1 tobacco genome. FV12-6 transgenic line produced the highest mRNA expression (7-fold) correlated to the highest accumulation of anti-Toxoplasma recombinant scFv antibody (0.52 % of the total soluble protein), followed by FV16-10 (0.25 %), FV17-7 and FV3-11 (0.18 %). The expression of TP60 transgene did not affect the growth of the transgenic plants and the segregation analysis of FV3, FV12, and FV16 in T1 generation confirmed the transgene integration within a single locus according to 3:1 Mendelian’s law. These findings indicated the possibility of using plants as a bio-factory for recombinant protein production. Abstrak Sistem tumbuhan kini telah mendapat banyak perhatian kerana mereka menyediakan risiko yang rendah terhadap pencemaran patogen dan kos pengeluaran, pengubahsuaian pasca-protein translasi yang sesuai, dan keupayaan penggabungan multimerik berbanding dengan tentera mamalia dan bakteria. Dalam kajian ini, kami berhasrat untuk menghasilkan antibodi serasi rantaian tunggal (scFv) yang mengekodkan protein anti-Toxoplasma, TP60 di Nicotiana tabacum cv. SR1. Eksplan daun N. tabacum telah dijangkiti oleh Agrobacterium tumefaciens strain LBA4404 yang menyimpan vektor dedua pCAMBIA1304 yang mengandungi gen TP60. Transforman putative disahkan melalui ujian kualitatif GUS dan GFP. Band-band yang diperhatikan pada saiz ramalan 914 bp mengesahkan kehadiran TP60 transgen dan transgen tersebut telah diintegrasi secara stabil dalam genom tembakau T0 dan T1. Individu transgenik FV12-6 menghasilkan ekspresi mRNA tertinggi (7 kali ganda) berkait dengan pengumpulan tertinggi antibodi rekombinan scFv anti-Toxoplasma (0.52% daripada jumlah protein larut), diikuti oleh FV16-10 (0.25%), FV17-7 dan FV3-11 (0.18%). Ungkapan transgenik TP60 tidak menjejaskan pertumbuhan tumbuhan transgenik dan analisis pemisahan FV3, FV12, dan FV16 dalam generasi T1 mengesahkan integrasi transgene dalam satu lokus mengikut 3: 1 undang-undang Mendelian. Penemuan ini menunjukkan kemungkinan menggunakan tumbuhan sebagai kilang bio untuk pengeluaran protein rekombinan. Keywords Agrobacterium-mediated transformation, plant molecular farming, recombinant protein, single-chain fragment antibody (scFv), tobacco, toxoplasmosis
EFFECT OF BIOCONTROL AGENTS ON DAMPING-OFF AND ROOT-ROT DISEASES OF QUINOA (CHENOPODIUM QUINOA WILLD) SEEDLING

uinoa, recently introduced to Egypt and is considered as an important crop as it can grow and give considerable yield in new reclaimed salt-affected soils. Isolation trials from rotted quinoa seedlings collected in Egypt (Fayoum governorate) yielded Fusarium solani, Alternaria sp. , Acremonium sp. and F. oxysporum. All the isolated fungi were pathogenic and caused damping-off and root-rot diseases. The efficiency of Streptomyces strains as a biocontrol agent against the pathogenic fungi was investigated under laboratory and greenhouse conditions. Streptomyces toxytricini showed the highest inhibition zone of the three strongest pathogenic fungi. S. toxytricini produced indole acetic acid (IAA) and exhibited optimum phosphate solubilization. Streptomyces strains mixtures reduced damping-off incidence, rootrot incidence and severity as well as significantly increased the number of viable seedlings under greenhouse conditions. Increase of phenolic content as well as activity of peroxidase and chitinase enzymes in Streptomyces-treated plants proved the biocontrolinduced systemic resistance in quinoa seedlings against a mixture of three pathogenic fungi; F. solani, Acremonium sp. and F. oxysporum.
Dispersal of Whitebark Pine Seeds by Clark's Nutcracker: A Mutualism Hypothesis

(1) Clark's nutcrackers (Nucifraga columbiana) store a mean of only 3.7 whitebark pine (Pinus albicaulis) seeds per cache, which reduces competition for moisture and space. The mean depth at which seeds are stored, 2.0 cm, is compatible with germination requirements, and many sites selected appear suitable for seed germination. (2) One nutcracker stores about 32 000 whitebark pine seeds at subalpine elevations each year, which represent 3-5 times its energetic requirements. Although parent nutcrackers feed stored seeds to juveniles, in some years there is probably an excess of seeds stored by the population as a whole. (3) Experimental results suggest that seeds retrieved from nutcracker caches are as viable as seeds extracted by hand from cones and that seedlings originating in nutcracker caches have a good survival rate: 56% over the first year and 25% by the fourth year. (4) In comparison with Clark's nutcracker, alternative disperal agents, i.e. rodents and cone disintegration, disperse fewer seeds, disperse seeds shorter distances from parent trees, place seeds in sites less suitable for germination, and/or make large seed caches which lower reproductive potential. Nutcrackers disseminate seeds throughout the subalpine habitat and are, in part, responsible for the 'pioneering' status of whitebark pine. (5) The evolution of wingless seeds and indehiscent cones in the Cembra pine group was probably a consequence of seed dispersal by an ancestral nutcracker form. It appears that the Clark's nutcracker-whitebark pine interaction is both coevolved and mutualistic.
Applying pathﬁnding techniques on the development of an Android game

A BSTRACT Mobile devices are present, more and more, in our daily life. As the most of these devices have low memory and low processing power, the developers need to be smart and creative to develop applications that can be performed quickly and efﬁciently. This work aims to face these limitations and apply artiﬁcial intelligence techniques, which usually require long time processing and large memory usage, on the development of an Android game. We chose the Android platform because it is the most popular nowadays for mobile devices. We developed a maze game, where the player must follow a path to reach a goal, escaping from enemies. The enemies use pathﬁnding techniques, trying to reach the player. Each enemy has been implemented with a different pathﬁnding technique, and the behaviors and performances were evaluated and compared. We implemented the uninformed (blind) search strategies breadth-ﬁrst search, depth-ﬁrst search and uniform-cost (ordered) search, and the informed (heuristic) search strategies greedy search and A* search. We observed, as expected, that techniques using information of the problem domain are more promising, because they can ﬁnd good paths and also run in a satisfactory time. Thus, we showed that it is possible to create an Android game, applying these meth-ods, ensuring its ﬂuidity and the user’s enjoyment.
Additive Hazard Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies

The case-cohort design is an effective and economical method in large cohort studies, especially when the disease rate is low. Case-cohort design in most of the existing literature is mainly used to analyze the univariate failure time data. But in practice, multivariate failure time data are commonly encountered in biomedical research. In this paper, we will propose methods based on estimating equation method for case-cohort designs for clustered survival data. By introducing the event failure rate, three different weight functions are constructed. Then, three estimating equations and parameter estimators are presented. Furthermore, consistency and asymptotic normality of the proposed estimators are established. Finally, the simulation results show that the proposed estimation procedure has reasonable finite sample behaviors.
Prediction of fume formation in a pulse d gas metal arc welding process by means of a 2D time-dependent arc model coupled with nanoparticle tracking

Fume formation in a pulsed gas metal arc welding (GMAW) process has been investigated by coupling a time-dependent axi-symmetric 2-D model, which takes into account both droplet detachment and production of metal vapour, with a model for fume formation and transport based on the method of moments (MoM) for the solution of the aerosol general dynamic equation.
Efficient Estimation of General Treatment Effects using Neural Networks with A Diverging Number of Confounders.

The estimation of causal effects is a primary goal of behavioral, social, economic and biomedical sciences. Under the unconfounded treatment assignment condition, adjustment for confounders requires estimating the nuisance functions relating outcome and/or treatment to confounders. The conventional approaches rely on either a parametric or a nonparametric modeling strategy to approximate the nuisance functions. Parametric methods can introduce serious bias into casual effect estimation due to possible mis-specification, while nonparametric estimation suffers from the "curse of dimensionality". This paper proposes a new unified approach for efficient estimation of treatment effects using feedforward artificial neural networks when the number of covariates is allowed to increase with the sample size. We consider a general optimization framework that includes the average, quantile and asymmetric least squares treatment effects as special cases. Under this unified setup, we develop a generalized optimization estimator for the treatment effect with the nuisance function estimated by neural networks. We further establish the consistency and asymptotic normality of the proposed estimator and show that it attains the semiparametric efficiency bound. The proposed methods are illustrated via simulation studies and a real data application.
Gaussian vectors with Markov property

We demonstrate the parallel between the properties of Gaussian vectors and the Euclidean geometry. In particular we study the Markov property and give various equivalent Euclidean and probabilistic characterizations. We also give a simple Euclidean proof of the conditional maximality of the differential entropy for the Markov Gaussian vector (related to the Burg's Theorem).
Data Quality Evaluation using Probability Models

This paper discusses an approach with machine-learning probability models to evaluate the difference between good and bad data quality in a dataset. A decision tree algorithm is used to predict data quality based on no domain knowledge of the datasets under examination. It is shown that for the data examined, the ability to predict the quality of data based on simple good/bad pre-labelled learning examples is accurate, however in general it may not be sufficient for useful production data quality assessment.
Barred Galaxies in the IllustrisTNG Simulation

Almost two-thirds of disk galaxies in the local universe host bars, which serve as important drivers of secular evolutionary processes. While cosmological simulations are powerful tools to study the formation and evolution of galaxies, they have often struggled to generate reasonable bar populations. We measure the fraction, size, and strength of bars in 3866 disk galaxies from the TNG100 run of the advanced cosmological simulation IllustrisTNG. Consistent with observations, about 55% of disk galaxies with stellar mass are barred, and the relation between bar size and total stellar mass is similar to that found in near-infrared surveys. However, the formation of bars is suppressed in galaxies with , which may result from the difficulty TNG100 has in resolving short bars with radius . In contrast, up to 75% of massive disk galaxies with have bars, ∼10%–20% higher than observed. TNG100 overproduces relatively short bars (radius ∼1.4–3 kpc) with respect to the mass–bar size relation observed in near-infrared surveys. Tracing the progenitors of z = 0 massive galaxies we find that the bar fraction increases from 25% to 63% between z = 1 and 0. Instead if we select all disk galaxies during z = 0–1 with a constant mass cut of we find that the bar fraction is a nearly constant 60%.
Co-evolution of Functional Brain Network at Multiple Scales during Early Infancy

The human brains are organized into hierarchically modular networks facilitating efficient and stable information processing and supporting diverse cognitive processes during the course of development. While the remarkable reconfiguration of functional brain network has been firmly established in early life, all these studies investigated the network development from a "single-scale" perspective, which ignore the richness engendered by its hierarchical nature. To fill this gap, this paper leveraged a longitudinal infant resting-state functional magnetic resonance imaging dataset from birth to 2 years of age, and proposed an advanced methodological framework to delineate the multi-scale reconfiguration of functional brain network during early development. Our proposed framework is consist of two parts. The first part developed a novel two-step multi-scale module detection method that could uncover efficient and consistent modular structure for longitudinal dataset from multiple scales in a completely data-driven manner. The second part designed a systematic approach that employed the linear mixed-effect model to four global and nodal module-related metrics to delineate scale-specific age-related changes of network organization. By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales, where each scale displays the unique reconfiguration pattern in terms of modular organization.
Generalized hypergeometric expansion related to the Hurwitz zeta function

We study the incomplete Mellin transformation of the fractional part and the related log-sine function when composed by an affine complex map. We evaluate the corresponding integral in two different ways which yields equalities with series in hypergeometric functions on each side. These equalities capture basic analytic properties of the Hurwitz zeta function like meromorphic extension and the functional equation. We give rates of convergence for the involved series. As a special case we find that the exponential rate of convergence for the deformation of the log-sine to the fractional part in the imaginary component is carried over to these equalities.