bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic diversity, population structure and selection signatures in Enset (Ensete ventricosum, (Welw.) Cheesman), an underutilized and key food security crop in Ethiopia
Authors: Haile Alye Tefera , Kovi Mallikarjuna Rao , Johnsen Sylvia Sagen , Tesfaye Bizuayehu , Hvoslef-Eide Trine , Rognli Odd Arne 
Published: July 2023

AbstractEnset (Ensete ventricosum (Welw.) Cheesman) is a multipurpose, drought-tolerant and a key food security crop, which is the staple food for peoples in the south and southwestern parts of Ethiopia. In this study, we characterized genetic diversity, population structure and selection signatures in 226 cultivated and 10 wild enset accessions collected from diverse enset growing regions of Ethiopia using 3505 high-quality single nucleotide polymorphism (SNP) markers obtained from double-digest restriction-site associated DNA (ddRAD) sequences. The population structure and cluster analyses clearly distinguished between cultivated and wild enset. The analysis of molecular variance (AMOVA) revealed higher levels of genetic diversity within populations and regions (91.2 and 92.4%, respectively) than between populations and regions (8.8 and 7.6%, respectively). This shows that the region of origin and environmental heterogeneity have little influence on the genetic diversity. However, the genetic differentiation between regions was moderate to large (FST = 0.06–0.17). The genetic structure of enset was mainly shaped by eco-geographic factors, mode of propagation and cultivation status. Six genes potentially involved in sexual reproduction and flowering signalling, which are key processes underlying domestication and adaptation, were under positive selection demonstrating that sexual reproduction plays an important role in shaping enset diversity. A lot of unexplored diversity is available for improving enset in Ethiopia, with patterns of diversity consistent with divergent selection on adaptive traits. This diversity also shows potential for introducing enset as a more food secure crop for the food insecure regions in the dry north of Ethiopia.
Top-down and bottom-up control of plankton structure and dynamics in hypertrophic fishponds
Authors: Vrba Jaroslav , Šorf Michal , Nedoma Jiří , Benedová Zdenka , Kröpfelová Lenka , Šulcová Jana , Tesařová Blanka , Musil Martin , Pechar Libor , Potužák Jan , Regenda Ján , Šimek Karel , Řeháková Klára 
Published: July 2023

AbstractWe investigated the effects of strong top-down control by high fish stock on structure and seasonal dynamics of plankton in nine fishponds under conventional fishery management based on auxiliary feeding during two vegetation seasons. Mean concentrations of total nitrogen, phosphorus, and high densities of phytoplankton, bacteria, heterotrophic nanoflagellates, and ciliates indicated hypertrophic state of the fishponds, as well as a markedly reduced control of these microbial food web components by crustacean zooplankton. Mean seasonal densities of zooplankton varied within one order of magnitude for cladocerans, copepods, nauplii, and rotifers. Daphnia were found in most fishponds in densities up to 630 ind. l−1 (median: 53 ind. l−1). While TN and TP concentrations were high, dissolved inorganic N (median: 29 µg l−1) and reactive P (median: 11 µg l−1) indicated possible nutrient deficiency. The fish stock index (defined as the product of biomass and square root of densities) was used as a proxy for fish predation pressure. Multivariate analysis revealed that nutrients and high fish stocks (market carp, carp fry, and/or undesirable small planktivorous fishes) were the main driving forces shaping the fishpond plankton. The resulting trophic structure thus severely reduced the herbivorous zooplankton–fish link during a vegetation season.
An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans
Authors: Franka Michelle , Edthofer Alexander , Körner Andreas , Widmann Sandra , Fenzl Thomas , Schneider Gerhard , Kreuzer Matthias 
Published: July 2023

AbstractAs electrical activity in the brain has complex and dynamic properties, the complexity measure permutation entropy (PeEn) has proven itself to reliably distinguish consciousness states recorded by the EEG. However, it has been shown that the focus on specific ordinal patterns instead of all of them produced similar results. Moreover, parameter settings influence the resulting PeEn value. We evaluated the impact of the embedding dimension m and the length of the EEG segment on the resulting PeEn. Moreover, we analysed the probability distributions of monotonous and non-occurring ordinal patterns in different parameter settings. We based our analyses on simulated data as well as on EEG recordings from volunteers, obtained during stable anaesthesia levels at defined, individualised concentrations. The results of the analysis on the simulated data show a dependence of PeEn on different influencing factors such as window length and embedding dimension. With the EEG data, we demonstrated that the probability P of monotonous patterns performs like PeEn in lower embedding dimension (m = 3, AUC = 0.88, [0.7, 1] in both), whereas the probability P of non-occurring patterns outperforms both methods in higher embedding dimensions (m = 5, PeEn: AUC = 0.91, [0.77, 1]; P(non-occurring patterns): AUC = 1, [1, 1]). We showed that the accuracy of PeEn in distinguishing consciousness states changes with different parameter settings. Furthermore, we demonstrated that for the purpose of separating wake from anaesthesia EEG solely pieces of information used for PeEn calculation, i.e., the probability of monotonous patterns or the number of non-occurring patterns may be equally functional.
On the Role of the Integrable Toda Model in One-Dimensional Molecular Dynamics
Authors: Benettin Giancarlo , Orsatti Giuseppe , Ponno Antonio 
Published: July 2023

AbstractWe prove that the common Mie–Lennard-Jones (MLJ) molecular potentials, appropriately normalized via an affine transformation, converge, in the limit of hard-core repulsion, to the Toda exponential potential. Correspondingly, any Fermi–Pasta–Ulam (FPU)-like Hamiltonian, with MLJ-type interparticle potential, turns out to be 1/n-close to the Toda integrable Hamiltonian,nbeing the exponent ruling repulsion in the MLJ potential. This means that the dynamics of chains of particles interacting through typical molecular potentials, is close to integrable in an unexpected sense. Theoretical results are accompanied by a numerical illustration; numerics shows, in particular, that even the very standard 12–6 MLJ potential is closer to integrability than the FPU potentials which are more commonly used in the literature.
Detecting social-ecological resilience thresholds of cultural landscapes along an urban–rural gradient: a methodological approach based on Bayesian Networks
Authors: Arnaiz-Schmitz C. , Aguilera P. A. , Ropero R. F. , Schmitz M. F. 
Published: July 2023

Abstract
Context
The difficulty of analysing resilience and threshold responses to changing environmental drivers becomes evident in the social-ecological systems framework due to their inherent complexity. Research is needed to develop new tools able to deal with such challenges and determine potential thresholds for SES variables that primarily influence tipping point behaviour.

Objectives
In this paper, a methodology based on the application of Bayesian Networks (BNs) has been developed to quantify the social-ecological resilience along an urban–rural gradient in Madrid Region, detecting the tipping point values of the main socioeconomic indicators implying critical transitions at landscape stability thresholds.

Method
To do this, the spatial–temporal trends of the landscape in an urban–rural gradient from Region de Madrid (Spain) were identified, to then quantify the intensity of the changes and explain them using BNs based on regression models. Finally, through inference propagation the thresholds of landscape change were detected.

Results
The results obtained for the study area indicate that the most resilient landscapes analysed are those where the traditional silvo-pastoral activity was maintained by elderly people and where there is cohesion between neighbouring rural municipalities.

Conclusion
The method developed has allowed us to detect the tipping points from which small changes in socioeconomic indicators generate large changes at the landscape level. We demonstrate that the use of BNs is a useful tool to achieve an integrated social-ecological spatial planning.
LifeSeeker: an interactive concept-based retrieval system for lifelog data
Authors: Nguyen Thao-Nhu , Le Tu-Khiem , Ninh Van-Tu , Caputo Annalina , Healy Graham , Smyth Sinéad , Tran Minh-Triet , Binh Nguyen Thanh 
Published: August 2023

AbstractLifelogging was introduced as the process of passively capturing personal daily events via wearable devices. It ultimately creates a visual diary encoding every aspect of one’s life with the aim of future sharing or recollecting. In this paper, we present LifeSeeker, a lifelog image retrieval system participating in the Lifelog Search Challenge (LSC) for 3 years, since 2019. Our objective is to support users to seek specific life moments using a combination of textual descriptions, spatial relationships, location information, and image similarities. In addition to the LSC challenge results, a further experiment was conducted in order to evaluate the power retrieval of our system on both expert and novice users. This experiment informed us about the effectiveness of the user’s interaction with the system when involving non-experts.
Flexible thermal camera solution for Smart city people detection and counting
Authors: Collini Enrico , Palesi Luciano Alessandro Ipsaro , Nesi Paolo , Pantaleo Gianni , Zhao William 
Published: August 2023

AbstractTourism management plays an important role in the context of Smart Cities. In this work, we have used thermal cameras for the development of an Object Detection solution in pedestrian areas. The solution can classify people, bikes, strollers, and count people in Real-Time by using telephoto and wide-angle thermal cameras, in hot squares where there is a relevant number of people passing by. This work has improved FASTER-R-CNN and YOLOv5 architectures with new data sets and fine-tuning approaches to enhance mean average precision and flexibility whether compared to state of the art solutions. Both top-down and bottom-up training adaptation approaches have been assessed in order to demonstrate that the proposed bottom-up approach can provide better results. Results have overcome the state-of-the-art in terms of mean Average Precision in counting (i) for relevant number of people in the scene (removing the limitation of previous state-of-the-art solutions that were set to provide good precision up to 10 people) and (ii) in terms of flexibility with respect to different kinds of camera and resolutions. The resulting model can produce results also when executed on thermal camera and in Real-Time on industrial PC of mid-level. The proposed solution has been developed and validated in the framework of the Herit-Data EC project and it has exploited the Snap4City platform for the final collection of data results, monitoring and their publication on real time dashboards.
On the numerical treatment of interlaced target values: modeling, optimization and simulation of regulating valves in gas networks
Authors: Hennings Felix , Petkovic Milena , Streubel Tom 
Published: August 2023

AbstractDue to the current and foreseeable shifts towards carbon dioxide neutral energy production, which will likely result in balancing fluctuating renewable energy generation by transforming power-to-gas-to-power as well as building a large-scale hydrogen transport infrastructure, the trading and transport operations of gas will become more dynamic, volatile, and hence also less predictable. Therefore, computer-aided support in terms of rapid simulation and control optimization will further broaden its importance for gas network dispatching. In this paper, we aim to contribute and openly publish two new mathematical models for regulators, also referred to as control valves, which together with compressors make up the most complex and involved types of active elements in gas network infrastructures. They provide direct control over gas networks but are in turn controlled via target values, also known as set-point values, themselves. Our models incorporate up to six dynamical target values to define desired transient states for the elements’ local vicinity within the network. That is, each pair of every two target values defines a bounding box for the inlet pressure, outlet pressure as well as the passing mass flow of gas. In the proposed models, those target values are prioritized differently and are constantly in competition with each other, which can only be resolved dynamically at run-time of either a simulation or optimization process. Besides careful derivation, we compare simulation and optimization results with predictions of the widely adopted commercial simulation tool SIMONE, serving as our substitute for actual real-world transport operations.
Sweet corn yield prediction using machine learning models and field-level data
Authors: Dhaliwal Daljeet S. , Williams Martin M. 
Published: July 2023

AbstractThe advent of modern technologies, acquisition of large amounts of crop management and weather data, and advances in computing are reshaping modern agriculture. These advancements have unlocked the power of data by providing valuable insights and more accurate yield predictions. This study utilizes a historic US sweet corn dataset to: (a) evaluate machine learning model performances on sweet corn yield prediction and (b) identify the most influential variables for crop yield predictions. The sweet corn data comprised field-level data for over a quarter-century period (1992–2018) from two primary commercial sweet corn production regions for processing, namely the Upper Midwest and the Pacific Northwest. Several machine learning models were trained to predict field-level sweet corn yield from 67 variables of crop genetics, management, weather, and soil factors. The random forest model outperformed all trained models with the lowest RMSE (3.29 Mt/ha) and the highest Pearson’s correlation coefficient (0.77) between predicted and observed yields. Variable importance plots revealed the top three most influential predictor variables as year (time), location (space), and seed source (genetics). Season long total precipitation and average minimum temperature during anthesis were the two most important weather variables in yield prediction. This is the first report of using fine-scale (time and space) crop data and advanced data analytics to leverage insights into commercial sweet corn production.
Do changes in perceived teacher behaviour predict changes in intrinsic reading motivation? A five-wave analysis in German lower secondary school students
Authors: Hawrot Anna , Zhou Ji 
Published: July 2023

AbstractAlthough several longitudinal studies have confirmed that need-supportive teacher behaviour shapes intrinsic motivation in school, longitudinal studies on its role for intrinsic reading motivation are lacking. To fill in this gap, this study investigated whether changes in selected aspects of student-perceived teacher need-supportive behaviour in German lessons predicted changes in intrinsic reading motivation. We also investigated how student intrinsic reading motivation and perceived teacher need-supportive behaviour changed over the course of lower secondary school. To this end, we used data of 7634 German students gathered between Grades 5 and 9 as part of the German National Educational Panel Study, five measurement occasions in total. The analyses, which involved univariate latent change score and change–change models, revealed decreases in perceived teacher need-supportive behaviour and intrinsic motivation between Grades 5 and 9. Moreover, the decreases in perceived teacher need-supportive behaviour in German lessons predicted decreases in intrinsic reading motivation. The study provides first evidence of longitudinal relationships between perceived teacher behaviour and intrinsic reading motivation. It also suggests that adjusting the classroom learning environment to student needs may contribute to alleviating the decrease in intrinsic reading motivation observed in multiple studies.
Inequalities in Health: Definitions, Concepts and Measurements—An Application in the Regional Health Authority in Italy
Authors: Renzi Pietro , Franci Alberto 
Published: August 2023

AbstractPolicymakers, researchers, and public health practitioners have long sought not only to improve overall population health but also to reduce or eliminate differences in health based on geography, race/ethnicity, socioeconomic status, and other social factors. Italian healthcare authorities and health policy makers are called to help to both solve existing inequities in accessing healthcare and remove barriers to healthcare. In this context, COVID-19 has highlighted the aspect of inequalities. Our paper proposes an overview of different methods of measuring health inequalities and their applications, both in regional and national contexts, in absolute and relative scales. The first involves an application on accessibility to an obstetrics clinic in a district of Marche region, connected to the educational level of the female population. The second, calculates the indices on inequalities in the infant mortality rate in the Italian regions in relation to income. Finally, the three indices are calculated considering the rate of self-perceived health in relation with the GDP per capita. To achieve this goal, we use the Kuznets index, the slope index of inequality and the concentration index as measures of social inequalities. The measurement of health inequities is an excitingly multidisciplinary endeavor. Its development requires interdisciplinary integration of advances from relevant disciplines. The proposed approach is one such effort and stimulates cross-disciplinary dialogues, specifically, about conceptual and empirical significance of definitions of health inequities.
Graphical Abstract
Modeling adsorption and photocatalytic treatment of recalcitrant contaminant on multi-walled carbon/TiO2 nanocomposite
Authors: Pete Kwena Yvonne , Kabuba John , Otieno Benton , Ochieng Aoyi 
Published: August 2023

AbstractA nanocomposite photocatalyst consisting of titanium dioxide (TiO2) supported on multiwalled carbon nanotubes (MWCNTs) has been successfully prepared and used for the treatment of wastewater contaminated with tetracycline (TC), a recalcitrant antibiotic pollutant. The TiO2/MCNT composites were prepared by a simple evaporation-drying method. The properties of MWCNT/TiO2 were optimized by dispersing different amounts of TiO2 onto MWCNT. The structural and optical characteristics of the nano-engineered photocatalyst composite were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) techniques. Photocatalytic degradation of TC was conducted in a quartz glass reactor. Different kinetic models were used to demonstrate the governing mechanisms. The findings revealed that the TiO2/MWCNT composite had enhanced photocatalytic activity (95% TC removal) compared to TiO2 (86% removal). The photocatalyst nanocomposite exhibited overall pseudo-second-order reaction kinetics and favored the Langmuir adsorption isotherm. Although up to 95% degradation of TC was achieved, only 75% of it was mineralized as a result of the formation of stable refractory intermediates.
Lesson learnt from static pulling tests on trees: an experimental study on toppling behaviour of complex foundations
Authors: Galli Andrea , Sala Cristian , Castellanza Riccardo , Marsiglia Andrea , Ciantia Matteo Oryem 
Published: August 2023

AbstractStandard procedures for stability assessment of unstable trees are based, among other, on the interpretation of on-site, non-destructive static pulling tests. To this goal, a simple phenomenological equation is usually adopted in professional agronomic practice, and an estimation of the ultimate toppling resistance is extrapolated by fitting the test data, without taking root geometrical parameters and soil mechanical properties into account. From a geotechnical point of view, however, the root plate of a tree plays the role of a “living foundation”, and its behaviour under toppling actions (like those produced by intense wind gusts) conceptually corresponds to the mechanical response of shallow foundations under rocking loads. In the paper, several static pulling tests on real-scale trees (some of them have been run until the complete collapse, after some unloading–reloading cycles) and some tests taken from the literature are considered in order to investigate the toppling behaviour. A possible new interpretative equation is proposed and critically compared with the existing one against experimental results. The new equation allows for a mechanically meaningful description of the toppling curve of the tree and accounts for strength and deformability issues. It allows to introduce innovative “performance-based” approaches, which are commonly neglected by practitioners and professional agronomists in this field. Nevertheless, the experimental results show that tree toppling is a complex phenomenon, and capturing its failure condition requires more advanced multi-mechanism models and second-order effects to be accounted for. From a practical point of view, the proposed equation, employed within the same standard interpretative procedure currently adopted in practice for pulling tests, seems to provide conservative estimations of “operational” values of the ultimate toppling resistance, and in perspective, it could be used to significantly optimize—when needed—the design of structural stabilizing interventions on potentially unstable trees.
Applicability of 2D algorithms for 3D characterization in digital rocks physics: an example of a machine learning-based super resolution image generation
Authors: Karimpouli Sadegh , Kadyrov Rail , Siegert Mirko , Saenger Erik Hans 
Published: July 2023

AbstractDigital rock physics is based on imaging, segmentation and numerical computations of rock samples. Due to challenges regarding the handling of a large 3-dimensional (3D) sample, 2D algorithms have always been attractive. However, in 2D algorithms, the efficiency of the pore structures in the third direction of the generated 3D sample is always questionable. We used four individually captured µCT-images of a given Berea sandstone with different resolutions (12.922, 9.499, 5.775, and 3.436 µm) to evaluate the super-resolution 3D images generated by multistep Super Resolution Double-U-Net (SRDUN), a 2D algorithm. Results show that unrealistic features form in the third direction due to section-wise reconstruction of 2D images. To overcome this issue, we suggest to generate three 3D samples using SRDUN in different directions and then to use one of two strategies: compute the average sample (reconstruction by averaging) or segment one-directional samples and combine them together (binary combination). We numerically compute rock physical properties (porosity, connected porosity, P- and S-wave velocity, permeability and formation factor) to evaluate these models. Results reveal that compared to one-directional samples, harmonic averaging leads to a sample with more similar properties to the original sample. On the other hand, rock physics trends can be calculated using a binary combination strategy by generating low, medium and high porosity samples. These trends are compatible with the properties obtained from one-directional and averaged samples as long as the scale difference between the input and output images of SRDUN is small enough (less than about 3 in our case). By increasing the scale difference, more dispersed results are obtained.