Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid larval development under time stress reduces adult life span through increasing oxidative damage

1. While a trade-off between larval development and adult life span is key to under stand why not all animals develop at their maximum rate and why life histories align along a fast-slow continuum, it has been rarely studied. More general, the physiological mechanisms underlying life-history trade-offs are poorly under stood and there is ongoing debate about the mediatory role of oxidative stress. 2. We explicitly investigated the role of oxidative stress in mediating the trade-off between larval development and adult life span in the damselfly Lestes viridis . We exposed larvae to time stress (by manipulating photoperiods) and manipulated oxidative stress levels using the mitochondrial uncoupler 2,4-dinitrophenol (DNP) that causes a reduced production of reactive oxygen species. In addition, we con sidered other costs of an accelerated development in terms of reductions in im mune function (the activity of phenoloxidase [PO]) and energy storage (fat content). 3. Larvae accelerated their development but not growth under time stress, allowing to identify costs of rapid development without confounding effects of rapid growth. Rapid development came at the cost of a much shorter life span,
Existence of variational source conditions for nonlinear inverse problems in Banach spaces

Abstract Variational source conditions proved to be useful for deriving convergence rates for Tikhonov’s regularization method and also for other methods. Up to now, such conditions have been verified only for few examples or for situations which can be also handled by classical range-type source conditions. Here we show that for almost every ill-posed inverse problem variational source conditions are satisfied. Whether linear or nonlinear, whether Hilbert or Banach spaces, whether one or multiple solutions, variational source conditions are a universal tool for proving convergence rates.
Sources, Transformation, and Fate of Dissolved Organic Matter in the Gravel Bar of a Prealpine Stream

Gravel bars (GBs) are hotspots of biogeochemical activity, likely impacting carbon dynamics in streams and rivers. However, it remains unclear how GBs process dissolved organic matter (DOM) received from stream water and groundwater. Here we investigate the spatial and temporal variability of DOM concentration and composition within a GB using DOM absorbance and fluorescence measurements. We found clear seasonal and diurnal patterns in DOM composition within the GB, indicating changing contributions of DOM sources and transformation processes. While DOM composition was characterized by more protein‐like DOM in summer, with increasing contributions of humic‐like DOM toward winter within the stream water and GB, groundwater DOM exhibited a strong aromatic and humic‐like character year‐round. DOM composition and concentration varied diurnally during autumn and winter within the GB and stream water, while seasonally higher groundwater inputs in summer likely muted the diurnal pattern, pointing to the importance of seasonal hydrological controls on DOM dynamics. Mixing model analysis showed GB DOM characteristics to differ from that predicted by stream water‐groundwater mixing within the GB, particularly in summer. Dissolved organic carbon concentration decreased along flow paths through the GB with increasing residence time, likely pointing to microbial uptake and/or adsorption to sediment surfaces within the GB, with concurrent clear shifts in DOM composition. While freshly produced and more humified DOM increased along GB flow paths, protein‐like fluorescence (C3) was removed, indicating the simultaneous production and removal of DOM. Together, our findings highlight the role of GBs in DOM removal and transformation, influenced by seasonal shifts in temperature and hydrodynamics.
Educate – mentor – nurture: improving the transition from initial teacher education to qualified teacher status and beyond

ABSTRACT Teacher recruitment and retention concerns across the world remain a high priority to ensure the best possible education opportunities for children and young people. Related to recruitment and retention, but also important in its own right, is the wellbeing of early career teachers. This study investigated the wellbeing of early career teachers in England and Australia to examine how best to provide early career support as a foundation for professional growth and longer-term retention. Survey responses from 67 newly qualified teachers in England and Australia, and five semi-structured interviews, provided rich insights into new teachers’ experiences, highlighting the overwhelming nature of the transition experience as new teachers struggled to adjust as they moved from the relative safety of the initial teacher education context to the reality of work in schools, in particular managing considerable workload which continued beyond the initial transition phase. Vital to successful transition were ongoing linkages between initial teacher education providers and employing schools, a supportive community of practice and bespoke mentoring. This has important policy implications, emphasising the need for personalised approaches to transition with high-quality mentoring during the first few years in the profession. An ‘educate – mentor – nurture’ model is proposed, to enable smoother and more supportive transitions, leading to professional growth and wellbeing.
Evidence for thematic representational structure in the mental lexicon

Semantic structure in the mental lexicon is often assumed to follow a hierarchical taxonomic structure grouping similar items. This study uses a hierarchical network clustering analysis of a massive word association dataset that does not primarily focus on concrete noun categories, but includes the majority of the words used in daily life. At this scale, we found widespread overlap between thematically organized clusters, arguing against a discrete categoric view of the lexicon. An empirical analysis focusing on taxonomic categories confirmed the widespread thematic structure even for concrete noun categories in the animal domain. Overall, this suggests that applying network clustering to word association data provides valuable insight into how large-scale semantic information is represented. This analysis leads to a different, more thematic topology than the one inferred from idealized small-scale approaches that sample only specific parts of the lexicon.
Attitudes towards big data practices and the institutional framework of privacy and data protection - A population survey (KIT Scientific Reports ; 7753)

A survey of the German population addressed attitudes towards scenarios of big data practices, i.e. price discrimination in retail, credit scoring, differentiations in health insurance and in employment, with features of using internet data, automated decision-making, and selling of data. The study analysed behavioural adaptations, protection measures, relations to demographics, personal value orientations, knowledge about computers, and attitudes about privacy and data protection.
Effect of different thyroidectomy in the treatment of differentiated thyroid carcinoma : a meta-analysis

Objectives: To explore the effect of different thyroidectomy in the treatment of differentiated thyroid carcinoma. Methods: We retrieved the relevant trials from several public databases, including PubMed, Medline, Springer, Elsevier Science Direct, Cochrane Library and Google scholar. Odds ratio (OR) and 95% confidence intervals (CI) of for the recurrence, complications, hypoparathyroidism and recurrent laryngeal nerve injury events were collected and calculated in a fixed-effects model (the Mantel-Haenszel method) or a random-effects model (the DerSimonian and Laird method) when appropriate. Results: A total of 9 separate studies consisting of 3343 patients (experimental group: 2201; control group: 1142) were included in this meta-analysis. The results of this meta-analysis showed that there were significant differences in the recurrence after operation (OR=0.21, 95% CI=0.12 to 0.39, P<0.01) and the rate of complications after operation (OR=2.77, 95% CI=1.75 to 4.41, P<0.01) between experimental group and placebo group, suggesting that the recurrence of patients after operation in experimental group may be lower than control group and the rate of postoperative complications of patients in experimental group may be higher than that of control group. The overall meta-analysis also showed that there were no significant differences in the hypoparathyroidism after operation (OR=1.79, 95% CI=0.56 to 5.69, P=0.32) and the recurrent laryngeal nerve injury after operation (OR=1.13, 95% CI=0.33 to 3.91, P=0.85) between the two groups, indicating that the rate of hypoparathyroidism and recurrent laryngeal nerve injury after operation of patients in experimental group may be equal to control group. Conclusions: Our results indicated that using total or near-total thyroidectomy in treatment of differentiated thyroid carcinoma may have lower recurrence but higher complications after operation.
Multislice Electron Scattering Simulations for Angstrom-scale Magnetic Measurements with 4D-STEM

Understanding how spins interact at the atomic scale is key for miniaturization of real, scalable devices. Lorentz transmission electron microscopy (LTEM) [1], holography [2-3], and differential phase contrast (DPC) imaging [4-5] have been standard imaging techniques local arrangement of spins, e.g. magnetic domains and topological structures such as skyrmions [6]. These techniques have been limited to spatial resolutions of a couple of nanometers [6-8]. While new methods such as 4D-STEM might enable magnetic measurements with angstrom-scale probes, extracting magnetic signals has been challenging because the electron scattering from the atomic potential is roughly 1000-fold stronger than scattering from the local magnetic moment at the angstrom-scale [4, 8].
Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images

Most computer aided pathology detection systems rely on large volumes of quality annotated data to aid diagnostics and follow up procedures. However, quality assuring large volumes of annotated medical image data can be subjective and expensive. In this work we present a novel standardization framework that implements three few-shot learning (FSL) models that can be iteratively trained by atmost 5 images per 3D stack to generate multiple regional proposals (RPs) per test image. These FSL models include a novel parallel echo state network (ParESN) framework and an augmented U-net model. Additionally, we propose a novel target label selection algorithm (TLSA) that measures relative agreeability between RPs and the manually annotated target labels to detect the “best” quality annotation per image. Using the FSL models, our system achieves 0.28-0.64 Dice coefficient across vendor image stacks for intra-retinal cyst segmentation. Additionally, the TLSA is capable of automatically classifying high quality target labels from their noisy counterparts for 60–97% of the images while ensuring manual supervision on remaining images. Also, the proposed framework with ParESN model minimizes manual annotation checking to 12-28% of the total number of images. The TLSA metrics further provide confidence scores for the automated annotation quality assurance. Thus, the proposed framework is flexible to extensions for quality image annotation curation of other image stacks as well.
The Role of Voice Suppression in Case Managers’ Job Satisfaction and Retention

With the goal of improving child well-being, child welfare agencies have begun to focus on the child welfare workforce and to advance strategies that address job satisfaction and retention. A qualitative approach was employed to gather the perspectives of case managers regarding these important issues. Ten foster care case managers participated through three focus groups. Responses were solicited using a semi-structured set of questions primarily focused on critical factors that affect job satisfaction and turnover. Through inductive coding, a prominent theme emerged regarding the suppression of case managers’ voices. Case managers described the suppression of their voices during decision-making in foster care cases by five types of actors, i.e., supervisors, judges, guardians ad litem, attorneys, and funding agency representatives. Further, they described the negative effects this experience had on both themselves and the children and families they serve. These results demonstrate the importance of inter-professional interactions in the foster care field. Further research is needed to identify the extent of this problem and the ways in which interactions can be improved and all voices can be considered.
Effect of Nitrogen Fertilization and Biostimulative Compounds on Onion Productivity

Abstract Two field experiments were carried out to investigate the optimum nitrogen rate (80, 100 and 120 kg N fed.−1; Fed = 0.38 ha) and stimulative compounds, i.e. foliar spraying with water, as control, Azotobacter spp. and Azospirillum spp. , yeast, compost tea and humic acid on vegetative growth, yield, quality, as well as storability of bulb yield of onion (Allium cepa L.) under North Delta conditions. The results showed that the vegetative growth was positive influenced, also yield its components, quality and storability of onion were related to the medium rate of nitrogen (100 kg N fed.−1). Furthermore, foliar spraying with humic acid at the rate of 1 kg fed. −1 led to a significant increment in the most of vegetative growth characteristics, as well as total bulb yield and its components, bulb quality and storability of onion. Both of 100 kg N fed. −1 and spraying humic acid at the rate of 1 kg fed. −1 significantly increased most vegetative growth characteristics, total and marketable bulbs yield fed. −1, bulb quality and storability of onion. So, this study concluded that onion farmers at North Delta of Egypt should fertilize onions with nitrogen at the rate of 100 kg N fed. −1 with spraying humic acid at the rate of 1 kg fed. −1 to achieve the highest economic yield.
Design and Implementation of Power Flow Management for PV Systems with Bi-directional Converter

This paper focuses on the control of power flow management system for photovoltaic (PV) applications with a high efficiency bi-directional converter. The proposed PV system involves a power source (PV array), a power sink (load), and power sources/sink (battery), and hence, a power flow management system is required to balance the power flow among these sources. One such system is developed for selecting the operating modes of the battery by sensing the dc link voltage. The bidirectional converter includes fewer switches, has less conduction and switching losses. The viability of the scheme has been ascertained by performing experimental studies on a laboratory proto-type. Keywords— Bidirectional converter; photovoltaic; power flow management system; DC link voltage _________________________________________________________________________________________________________________
Deep Learning-Based Water Segmentation for Autonomous Surface Vessel

Visual-based obstacle detection from an autonomous surface vessel (ASV) is a complex task due to high variance of scene properties such as different illumination and presence of reflections. One approach in implementing the task is through extracting waterlines to enable inferring of vessel orientation and obstacles presence. Classical computer vision algorithms for detection holds limitation in robustness and scalability. With recent breakthroughs in deep neural network architectures, vision-based object detection is seen to obtain high performance. In this work, the deep learning models based on Convolutional Neural Network (CNN) to implement binary semantic segmentation is studied. This architecture identifies each pixel to water and non-water classes. In purpose of benchmarking models, Fully Convolutional Network (FCN), SegNet and U-Net are trained on a publicly available dataset, IntCatch Vision Data Set (ICVDS), to evaluate the performance. From the experiments carried out, quantitative results show effectiveness of the models with accuracy all above 95.55% and lowest average speed of 11 frames per second. To improve, pre-trained networks (VGG 16, Resnet-50 and MobileNet) are used as a backbone, obtaining an improved accuracy above 98.14% with lowest inferring speed of 10 frame per second. Using the developed ASV, new dataset of 143 images called Malaysia ASV Dataset (MASVD) is collected, labelled and made publicly available. The trained models are tested with the newly collected dataset obtaining accuracy of 75%. The high accuracy performance shows potential for the models to be employed for collision avoidance algorithm in ASV navigation.
Discriminating performance profiles of cycling disciplines

The aim of this study was to document the athletic profiles of adolescent and young adult high performance cycling athletes and to investigate to what extent different cycling disciplines can be discriminated from each other based on a generic test battery. A total of 243 adolescent (12.0- 15.99 y) and 63 young adult (>=16 y) male cyclists from road cycling, track cycling, cyclo-cross, and mountain bike (MTB) participated in the study. All participants performed four anthropometric, five physical, three motor coordination, and two cycling-specific tests. Using discriminant analyses, the young adult athletes could be correctly classified to their discipline for 80.7%. Track cyclists outperformed the other disciplines in explosive and coordinative skills while for cyclo-cross, and particularly MTB, the performance on the shuttle bike test seemed to be a distinctive variable. Road cyclists however, showed a significant overlap in performance characteristics with the other disciplines. In spite of the less pronounced discriminative character in the adolescent cycling population (51.0%), the discriminative characteristics are in line with the results of the young adults. This study allows to orient cyclists towards their best-fitted discipline in young adulthood. The relevance of these findings for coaches, experts and federations with respect to early/late specialization, and talent orientation are discussed.
Tectonic and Structural Relationships in Silvania Mountains

– Tectonic and Structural Relationships in Silvania Mountains. The structural landforms were defined by the fault systems present in the area, as dislocations following the Alpine orogenic system, which imposed the subaerial and submerged dynamics of the landforms, coordinating in this way the modeling systems with the geological time scale, on the basis of complex tectonic and structural, morphotectonic and morphoselection relationships between the geologic structure, the tectonic factors and the external geodynamic agents. The Meseș hemi‐anticline is different from the horst of Plopiş as well as Șimleu and Coșeiu Hillocks due to the dynamics, intensity and magnitude of the faulting phenomenon which controlled the sedimentation process and the placement of eruptive bodies. The morphoselection relationships, in which the main role belonged to the selective erosion, facilitated the development of three types of structural (tabular, monoclinal and folded) landforms.
Mobility and urban quality of life: a comparison of the hedonic pricing and subjective well-being methods

ABSTRACT There is no consensus about the relation between urban scale and quality of life. Quality of life in Oslo and the rest of Norway is compared using two commonly employed methods to measure geographical variation in quality of life: hedonic pricing based on the Rosen–Roback model and analysis of subjective well-being. Since the hedonic pricing approach assumes that households are perfectly mobile, the population is divided into mobility quartiles. The methods arrive at the same conclusion for the most mobile population group – quality of life is higher in Oslo – but different conclusions are reached for other mobility groups.
A Collaborative Prediction of Presence of Arrhythmia in Human Heart with Electrocardiogram Data using Machine Learning Algorithms with Analytics

Human heart is the major organ of human being which could fail the other systems in the body at the same time. Hence predicting heart disease is one of the challenging researches that requires meticulous analysis of heart rhythms properly. The irregular heart rhythms or beat is referred to as the Arrhythmia where heart rhythms with low or high rates comparing to the normal heart beat rate which ranges from 60 to 100 beats per minute. The heartbeat can be monitored and identified with the electrical disorder disease called Arrhythmia. This is very deadly when untreated for a long time as mortality rate is extremely high. Hence a prediction system is required to identify the irregular nature of heart and predict the heart problem in the future. The major objective of this research paper is to predict the presence of arrhythmia which is caused as a result of electrical imbalance and irregular heart beat in human being. The prediction is formulated with the help of essential parameters from electrocardiogram like age, gender, height, weight, BMI, QRS duration, P-R interval, Q-T interval, T interval, P interval, QRS, T, P, QRST, J values which will help the prediction of Arrhythmia in human to the best. The dataset sample is collected from UCI Repository based on electrocardiogram report values and pre-processed using Mat lab. The data is converted into test data and prediction is expected to be completed using Machine Deep learning Algorithms as they could be the best models for disease or syndrome predictions. Finally, the Analytics is carried out using Rapid Miner Studio where machine learning algorithms is applied and results obtained. The research will be a starter for futuristic research on automatic prediction of heart disease in human beings with various other parameters.
The duality of science: Knowledge in information systems research

Rigorous research in Information Systems requires an understanding of how scientific foundations drive both the process and outcome. This science duality addresses the effective application of the scientific method (process) as well as the advance of knowledge (outcome) from the research project. This commentary responds to Siponen and Klaavuniemi’s paper with a focus on the uses of knowledge in Information Systems research.