Miracle-dz/UniFAR-FTUs huggingface dataset:
Bird species responses to forest-savanna boundaries in an Amazonian savanna

Understanding how species respond to habitat boundaries in a landscape is essential because such responses influence several ecosystem processes and services. However, most studies to date are focused on boundaries between natural and human-made ecosystems, with few investigating species' responses to boundaries between natural habitats. Using a two-step approach, this paper examined how birds respond to gallery forest-savanna boundaries in an Amazonian savanna in Amapá, Brazil. First, we quantified bird species' abundance and boundary sensitivity. Then, we used phylogenetic linear regression to evaluate if five species-level characteristics (trophic level, dispersal ability, body size, niche breadth, and rarity) can be used as predictors of species' boundary sensitivities. We recorded 113 species, of which 67 had enough records to assess their responses to habitat edges. Most species (89%) crossed the boundaries between forests and savannas, with only seven species restricted to one of the habitats. Rarity is the only species trait that predicts a species' boundary sensitivity. Our results show that studies to date have underestimated the magnitude of the biological dynamics occurring along neotropical forest-savanna boundaries
Prediction of Multiple Molten Iron Quality Indices in the Blast Furnace Ironmaking Process Based on Attention-Wise Deep Transfer Network

Molten iron quality (MIQ) indices prediction based on data-driven models is an important way to monitor product quality and smelting status in the blast furnace ironmaking process. However, some challenges still place in the MIQ prediction: 1) limited nonlinear and dynamic description capabilities and interpretability of data-driven models; 2) high demand on the number of the labeled samples; and 3) insufficient exploration of the underlying relationship between MIQ indices. In this case, we propose a novel data-driven deep model for the online prediction of MIQ indices. First, we design an attention-wise module to self-learn the nonlinear and dynamic relationship between process variables and prediction targets and enhance interpretability. Then, the minute-level molten iron temperature (MIT) data detected by our previously developed equipment is used to pretrain the attention-wise deep network to obtain the improved weights and reduce dependence on labeled samples. Finally, the pretrained model is extended to a structure with a weight-shared attention-wise module and task-separated prediction networks to explore the relationship between multiple prediction tasks. The effectiveness of the proposed attention-wise deep network is verified in an industrial ironmaking plant, which shows a significant improvement in performance, i.e., high accuracy and interpretability.
Wearable Cardiorespiratory Sensors for Aerospace Applications

Emerging Air Traffic Management (ATM) and avionics human–machine system concepts require the real-time monitoring of the human operator to support novel task assessment and system adaptation features. To realise these advanced concepts, it is essential to resort to a suite of sensors recording neurophysiological data reliably and accurately. This article presents the experimental verification and performance characterisation of a cardiorespiratory sensor for ATM and avionics applications. In particular, the processed physiological measurements from the designated commercial device are verified against clinical-grade equipment. Compared to other studies which only addressed physical workload, this characterisation was performed also looking at cognitive workload, which poses certain additional challenges to cardiorespiratory monitors. The article also addresses the quantification of uncertainty in the cognitive state estimation process as a function of the uncertainty in the input cardiorespiratory measurements. The results of the sensor verification and of the uncertainty propagation corroborate the basic suitability of the commercial cardiorespiratory sensor for the intended aerospace application but highlight the relatively poor performance in respiratory measurements during a purely mental activity.
Directed Biosynthesis of New to Nature Alkaloids in a Heterologous Nicotiana benthamiana Expression Host

Plants produce a wide variety of pharmacologically active molecules classified as natural products. Derivatization of these natural products can modulate or improve the bioactivity of the parent compound. Unfortunately, chemical derivatization of natural products is often difficult or impractical. Here we use the newly discovered biosynthetic genes for two monoterpene indole alkaloids, alstonine and stemmadenine acetate, to generate analogs of these compounds. We reconstitute these biosynthetic genes in the heterologous host Nicotiana benthamiana along with an unnatural starting substrate to produce the corresponding new-to-nature alkaloid product.
Quantifying Award Network and Career Development in the Movie Industry

In show business, awards are conferred to persons and films to provide incentives to performers’ future career development through periodic film festivals and events. In this work, we focused on exploring the growth and dynamics of the film award system, the structure of the award network, and the relationships between historical performance, collaborations, and future career success of performers in the movie industry. We collected data from IMDb, which covers more than 3.5K movie events for 520K individuals with their award-winning and career records for over 90 years. By using network analysis and regression models, we find several novel results. At first, we found the exponential proliferation of awards across all genres of films and all professions of individuals and the uneven distribution of the number of awards in careers across time. More than 30% of the performers have won multiple awards. Second, we built an award network to reveal the interlocks between awards based on multiple award-winning phenomena. We found that for prestigious awards, 47% of the linkages were over-representative than the expectations from the null model. Furthermore, the performers’ collaboration network was highly clustered, exhibiting a high propensity of linkages between awarded performers. Lastly, our regression models revealed that multiple factors were related to performers’ early career success and award winning. Specifically, we showed that along with the performers’ historical achievements, their collaborators serve an important role in award winning after being nominated, with the scope and depth of the impact differing in the awards’ prestige. This work has strong implications for the harmonious dynamics of the movie industry and the career development of performers.
The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method.
A Modified Event Grouping Based Algorithm for The University Course Timetabling Problem

This paper presents a study of a modified event grouping based algorithm (MEGB) for university course timetabling problem (UCTP). Multiple models to describe the problem and multiple approaches to solving it are pointed out. The main idea of the modification is that through reduction on the generated solutions the execution time of the standard event grouping based algorithm (EGB) will be reduced, too. Also, an implementation of the modified algorithm based on the described approach is presented. The methodology, conditions and the aims of the experiment are described. The experimental results are analyzed and conclusions are made. When increasing the number of the groups, the execution time of the MEGB algorithm increases and equates with the execution time of the EGB algorithm. The best results are obtained with the first 30% of the groups formed. In these groupings, the execution time of the MEGB algorithm is much less than the execution time of the EGB algorithm. This is because, in the EGB algorithm, every change in the event ordinance creates a new timetable, and all events are repositioned on it. This process is optimized by creating a partial timetable, whereby the ordinance of events in groups before the current does not change. In addition, a comparative analysis between the MEGB algorithm and two other algorithms for UCTP, respectively a genetic algorithm with the local search method (GALS) and a local search algorithm based on chromatic classes (CCLS) is made as well. The obtained results show that the MEGB algorithm and the CCLS algorithm generate better solutions for smaller input data sets, while the GALS algorithm generates better solutions for larger input data sets. However, in terms of the execution time, it was ascertained that the GALS algorithm runs slowest among the others.
Predicting spatiotemporal variability in radial tree growth at the continental scale with machine learning

Abstract Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are representative for large geographical regions remains an open issue. To overcome the limitations of interpreting a sparse network of sites, we propose an upscaling approach for annual tree-ring indices that approximate forest growth variability and compute gridded data products that generalize the available information for multiple tree genera. Using regression approaches from machine learning, we predict tree-ring indices in space and time based on climate variables, but considering also species range maps as constraints for the upscaling. We compare various prediction strategies in cross-validation experiments to identify the best performing setup. Our estimated maps of tree-ring indices are the first data products that provide a dense view on forest growth variability at the continental level with 0.5° and 0.0083° spatial resolution covering the years 1902–2013. Furthermore, we find that different genera show very variable spatial patterns of anomalies. We have selected Europe as study region and focused on the six most prominent tree genera, but our approach is very generic and can easily be applied elsewhere. Overall, the study shows perspectives but also limitations for reconstructing spatiotemporal dynamics of complex biological processes. The data products are available at https://www.doi. org/10.17871/BACI.248.
Sustainable Protocol for the Synthesis of 2′,3′-Dideoxynucleoside and 2′,3′-Didehydro-2′,3′-dideoxynucleoside Derivatives

An improved protocol for the transformation of ribonucleosides into 2′,3′-dideoxynucleoside and 2′,3′-didehydro-2′,3′-dideoxynucleoside derivatives, including the anti-HIV drugs stavudine (d4T), zalcitabine (ddC) and didanosine (ddI), was established. The process involves radical deoxygenation of xanthate using environmentally friendly and low-cost reagents. Bromoethane or 3-bromopropanenitrile was the alkylating agent of choice to prepare the ribonucleoside 2′,3′-bisxanthates. In the subsequent radical deoxygenation reaction, tris(trimethylsilyl)silane and 1,1′-azobis(cyclohexanecarbonitrile) were used to replace hazardous Bu3SnH and AIBN, respectively. In addition, TBAF was substituted for camphorsulfonic acid in the deprotection step of the 5′-O-silyl ether group, and an enzyme (adenosine deaminase) was used to transform 2′,3′-dideoxyadenosine into 2′,3′-dideoxyinosine (ddI) in excellent yield.
Preparation of Chitosan and Glycol Chitosan Coated Magnetic Nanoparticles Loaded with Carboplatin as Anticancer Drug

Surface modified Fe3O4 nanoparticles (Fe3O4-OA) with an average diameter of 10 nm were synthesized, coated by chitosan (CS) and glycol chitosan (GCS), thus magnetic polymer nanocomposites were obtained. The magnetic nanostructures were analyzed by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD) and vibrating sample magnetometer (VSM). All magnetic structures synthesized in this study exhibited superparamagnetic properties. Loading carboplatin (CPt) as anticancer drug to Fe3O4-OA-GCS nanocomposites were carried out with 13.17 % drug content and 38 % encapsulation efficiency. The cytotoxicity studies were occured by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay on L929 mouse fibroblasts and MCF-7 human breast cancer cells. Fe3O4-OA-GCS-CPt nanocomposites showed higher cytotoxicity than free CPt on the MCF-7 cells at 50 µg/ml drug concentrations during 72 h.
Single-Byte Error-based Practical Differential Fault Attack on Bit-Sliced Lightweight Block Cipher PIPO

With the recent development of the Internet of Things (IoT), related device use is increasing rapidly. As a result, accessing and hijacking the devices is an increasing security threat. The challenges of side-channel security of IoT devices are having a way of coming to the surface due to this physical accessibility. Accordingly, there is active research on lightweight block ciphers to provide security even in resource-scarce environments situations such as IoT. The bit-sliced structure increases memory and time efficiency using an implementation method that replaces a lookup table with a bit-wise operation. Therefore, it is a widely-used design technique for lightweight block ciphers. In this paper, we show a differential fault attack study, a type of side-channel analysis, targeting bit-sliced block ciphers. In particular, it proposes a novel attack rationale on the recently proposed lightweight block cipher PIPO and shows that it applies sufficiently to other bit-sliced block ciphers. The proposed attack is based on a more alleviated attacker’s assumption than the previously proposed attack, and it shows that less than 32 fewer fault ciphertext may fully recover the 128-bit of the PIPO 64/128 secret key. It proves that the attack is practical by verifying the attack through the actual electromagnetic fault injection. It also discusses the applicability of various bit-sliced block ciphers and shows how redundancy-based countermeasures might improve fault-robustness. Therefore, when using the bit-sliced block ciphers on IoT devices, we recommend applying appropriate countermeasures against fault injection attacks.
The Accredited University of Tomorrow: Accreditation, Creativity and International Recognition

Numerous challenges have impeded the Bologna process which was designed to provide guidelines for the establishment of appropriate academic standards at the university level. The Bologna process was expected to include a set of standards for the creation of independent accrediting bodies to provide accreditation for universities and higher colleges of education in the European Union, so that the work of students might be recorded and accepted as transferable among universities accepting the Bologna process. This paper provides initially a reflection on the nature and importance of accreditation standards. This follows consideration of the demands for transparency and accountability and consideration of the problems of recognizing transferable credits which are faced by higher educational institutions in Europe. The value and the benefits which derive from acceptable standards for institutional accreditation are identified. Identification is then followed by description of the five standards which will move the accreditation introverts in Europe to the more extravert approach being taken by universities in the United States and the rest of the world.
Form factors of local operators in the algebraic Bethe ansatz

We study integrable models solvable by the nested algebraic Bethe ansatz and possessing the GL(3)-invariant R-matrix. We consider a composite model where the total monodromy matrix of the model is presented as a product of two partial monodromy matrices. Assuming that the last ones can be expanded into series with respect to the inverse spectral parameter we calculate matrix elements of the local operators in the basis of the transfer matrix eigenstates. We obtain determinant representations for these matrix elements. Thus, we solve the inverse scattering problem in a weak sense.
Early Introduction of Allergenic Foods and the Prevention of Food Allergy

The increasing prevalence of food allergies is a growing public health problem. For children considered high risk of developing food allergy (particularly due to the presence of other food allergies or severe eczema), the evidence for the early introduction of allergenic foods, and in particular peanut and egg, is robust. In such cases, the consensus is clear that not only should such foods not be delayed, but that they should be introduced at approximately 4 to 6 months of age in order to minimize the risk of food allergy development. The early introduction of allergenic foods appears to be an effective strategy for minimizing the public health burden of food allergy, though further studies on the generalizability of this approach in low-risk populations is needed.
Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.
How Difficult Was It? Metacognitive Judgments About Problems and Their Solutions After the Aha Moment

The insight phenomenon is thought to comprise two components: cognitive and affective (the Aha! experience). The exact nature of the Aha! experience remains unclear; however, several explanations have been put forward. Based on the processing fluency account, the source of the Aha! experience is a sudden increase in processing fluency, associated with emerging of a solution. We hypothesized that in a situation which the Aha! experience accompanies the solution in, the problem would be judged as less difficult, regardless of the objective difficulty. We also planned to confirm previously discovered associations between the Aha! experience and accuracy, confidence, and pleasure. To test the proposed hypothesis, during the preliminary stage of the study, we developed a set of 100 remote associate problems in Russian (RAT-RUS) and asked 125 participants to solve problems and indicate the Aha! moment (after solution generation or solution presentation), confidence, difficulty, and likability of each problem. As expected, the Aha! experience often accompanied correct solutions and correlated with confidence judgments. We also found a positive correlation between the Aha! experience and problem likability. As for the main hypothesis, we confirmed that the Aha! experience after the presentation of the solution was associated with a decrease in subjective difficulty. When participants could not solve a problem but experienced the Aha! moment after the solution was presented to them, the problem was perceived as easier than one without the Aha! experience. We didn’t find the same effect for the Aha! after solution generation. Thus, our study partially supports the processing fluency account and demonstrates the association between the Aha! experience and metacognitive judgments about the accuracy and difficulty of problems.
The impact transconductance parameter and threshold voltage of MOSFET’s in static characteristics of CMOS inverter

The objective of this paper is to research the impact of electrical and physical parameters that characterize the complementary MOSFET transistors (NMOS and PMOS transistors) in the CMOS inverter for static mode of operation. In addition to this, the paper also aims at exploring the directives that are to be followed during the design phase of the CMOS inverters that enable designers to design the CMOS inverters with the best possible performance, depending on operation conditions. The CMOS inverter designed with the best possible features also enables the designing of the CMOS logic circuits with the best possible performance, according to the operation conditions and designers’ requirements.