Miracle-dz/UniFAR-FTUs huggingface dataset:
ects of long-term fungicide applications on microbial properties in tallgrass prairie soil

Several studies investigating the role of arbuscular mycorrhizal (AM) fungi in plant communities have included manipulations of AM fungal symbiosis using the fungicide benomyl. The objectives of this study were to evaluate the potential non-target eects of benomyl on soil biota and nutrient cycling in tallgrass prairie and to determine how mycorrhizae may in ̄uence these belowground properties. To accomplish these objectives, soil samples were collected during the 1996±1997 growing seasons from long-term benomyl-treated plots established on tallgrass prairie (Manhattan, KS) in 1991, and the following measurements were made: total bacterial and fungal biomass; abundance of nematodes; microbial biomass carbon and nitrogen; substrate-induced respiration; and potentially mineralizable C and N. Long-term benomyl applications resulted in an 80% reduction in mycorrhizal root colonization. By reducing root colonization, benomyl applications resulted in signi®cant decreases in total bacterial biomass and abundance of fungal-feeding and predatory nematodes (20, 12 and 33% reductions compared to control, respectively). Total microbial potential activity (i.e., substrate-induced respiration) increased by 10% with benomyl treatment, whereas the relative contribution of fungi to total microbial activity decreased signi®cantly with benomyl applications. In addition, microbial biomass C increased from 1364 (251.2 SE) to 1485 (2 51.2 SE) with benomyl treatment, and total carbon increased signi®cantly (08%) only in annually burned soils treated with benomyl. The magnitude of benomyl eects on soil components and processes were small (<33% change with benomyl) relative to eects on mycorrhizal root colonization (80% decrease with benomyl). These results indicate that rather than having large non-target eects, benomyl applications principally aect mycorrhizal root colonization, thereby indirectly in ̄uencing soil biota and nutrient availability. Results also indicate that mycorrhizal fungi play an important role in altering the availability and ̄ow of carbon in prairie soil and may in ̄uence the composition and abundance of groups of some soil biota. Changes in soil organisms and nutrient availability associated with altered mycorrhizal symbiosis may in ̄uence aboveground plant species responses to mycorrhizal suppression, but further research is needed to understand these potential eects. 7 2000 Elsevier Science Ltd. All rights reserved.
Tight bounds on adjacency labels for monotone graph classes

A class of graphs admits an adjacency labeling scheme of size $b(n)$, if the vertices in each of its $n$-vertex graphs can be assigned binary strings (called labels) of length $b(n)$ so that the adjacency of two vertices can be determined solely from their labels. We give tight bounds on the size of adjacency labels for every family of monotone (i.e., subgraph-closed) classes with a well-behaved growth function between $2^{O(n \log n)}$ and $2^{O(n^{2-\delta})}$ for any $\delta>0$. Specifically, we show that for any function $f: \mathbb N \to \mathbb R$ satisfying $\log n \leqslant f(n) \leqslant n^{1-\delta}$ for any fixed $\delta>0$, and some~sub-multiplicativity condition, there are monotone graph classes with growth $2^{O(nf(n))}$ that do not admit adjacency labels of size at most $f(n) \log n$. On the other hand, any such class does admit adjacency labels of size $O(f(n)\log n)$. Surprisingly this tight bound is a $\Theta(\log n)$ factor away from the information-theoretic bound of $\Omega(f(n))$. The special case when $f = \log$ implies that the recently-refuted Implicit Graph Conjecture [Hatami and Hatami, FOCS 2022] also fails within monotone classes. We further show that the Implicit Graph Conjecture holds for all monotone \emph{small} classes. In other words, any monotone class with growth rate at most $n! \,c^n$ for some constant $c>0$, admits adjacency labels of information-theoretic order optimal size. In fact, we show a more general result that is of independent interest: any monotone small class of graphs has bounded degeneracy.We conjecture that the Implicit Graph Conjecture holds for all hereditary small classes.
Measurement of Fluid Viscosity Using Lensless Digital Holography

A novel method for viscosity measurement of Newtonian fluids is proposed using lensless digital in-line holography. The holographic imaging of gravity driven motion of particles through the test fluid is performed. Numerical reconstruction of digital holograms provides 3-D particle imaging within the measurement volume. The particle size estimation and localization is performed using transform-based image processing. The particle velocity is estimated using the particle location at different time instants which is used to determine the fluid viscosity. The comparison of the proposed method with the conventional rheometer indicates its practical applicability with lesser experimental setup complexity and measurement time.
Artificial Intelligence as a Basic and Centre for Integrated Digital Bureaucracy

Digital technology, whose deepest spirit is artificial intelligence, seems to be man's last love. It was created to serve man so that his stutus glory is perfected by the results of his mind, and the mind is a gift from God of the universe that is complete. The purpose of this research is to describe the use of artificial intelligence technology to support the bureaucratic system in our country. This research is descriptive, with the methodological basis being qualitative research. The research method is a literature study, with the main sources being writings sourced from scientific journals, mass media publications and other relevant sources. The writings are elaborately narrated with the guidance of relevant theories. The results of the study found several factors in the implementation of this digital bureaucracy, namely (1) Availability of an integrated system, (2) Placement of human resources according to their fields and capabilities, (3) Sustainability of the program and (4) Periodic improvement of infrastructure (software and hardware) as needed.
Is it the end of the Cerrado domain in the west of state of Paraná (Brazil)? Floristic aspects of Cerrado fragments in Campo Mourão

Abstract Currently only a few relicts of Brazilian savanna (Cerrado domain) remain sparsely distributed in state of Paraná. This study catalogued the species of vascular plants occurring in three Cerrado fragments of Campo Mourão. We analysed 4,125 herbarium specimens, 952 of which were collected in the Cerrado fragments, representing 368 species (25 exotic) distributed in 70 families and 241 genera. Fabaceae, Asteraceae, Myrtaceae, Euphorbiaceae, Poaceae, Bignoniaceae, Malvaceae and Apocynaceae were the richest families. Sixty species (16%) were only collected before 1999, with no re-collections after 2000. Among the native species, 51 are endemic to Brazil, and three are confined to the state of Paraná. Six species are regarded as officially endangered in Brazil. We also present the first record of Jacaranda mutabilis for Southern Brazil. Forty-two species are probably locally extinct in Campo Mourão. In the three Cerrado fragments within the municipality, the forest phytophysiognomy is already predominant in these areas, indicating the expansion of Semideciduous Seasonal Forest within the Cerrado fragments. In addition, the presence of exotic species and the lack of legal, environmental protection in the two largest fragments put the future existence of these Cerrado fragments from Campo Mourão at risk.
LSTM-based Process Noise Covariance Prediction for AUV Navigation

The significance of process noise covariance cannot be overstated when conducting Extended Kalman Filter (EKF) for Autonomous Underwater Vehicles (AUVs). Regrettably, there currently exists no direct methods to accurately determine the values of process noise covariance in a specific AUV motion model. As a consequence, the selection of process noise covariance values is often reliant on experiential knowledge or specific experiments, leading to sub-optimal outcomes. Consequently, the implementation of EKF typically falls short of achieving optimal results. In this study, a novel approach for predicting the process noise covariance in an AUV navigation system based on a Long Short-Term Memory (LSTM) network is presented. The LSTM network takes into account the velocity measurements obtained from the Doppler Velocity Log (DVL), as well as the combined acceleration and attitude angle data obtained from the Inertial Navigation System (INS), and provides predictions for the process noise covariance. We have verified the effectiveness of the proposed algorithm through sea trials conducted in Tuandao Bay, Qingdao, China, using the Sailfish-210 AUV. The results demonstrate that the LSTM-based method outperforms the traditional EKF approach, efficaciously reducing errors in AUV position prediction and enhancing navigation accuracy.
Climate Change Impact on Solar Power Generation: A Sustainable Energy Planning Perspective for Kenya

: In the wake of the rising impacts of climate change, solar energy is poised to be crucial in the future of global energy security and sustainable development. However, in developing countries such as Kenya, there are limited studies on the consequences of climate change on solar power generation. This study was conducted with the aim of assessing how climate change affects solar power generation in Kenya, with the goal of informing strategies to bolster its resilience, adaptability, and overall energy planning. From the theoretical background of sustainable development, the research utilized a case study design using secondary data sources to unveil multifaceted aspects. Key secondary data examined included key policy documents and other documents retrieved from official websites. Through document content analysis, the evaluation focused on the vulnerability of solar power resources to climate change impacts by categorizing the findings into three main themes: the impact of climate change on solar energy generation, strategies for mitigation and adaptation, and policy implications for Kenya's overall energy planning. The results revealed a pressing need for explicit studies on how climate change affects solar power generation in Kenya. Estimating these potential impacts is crucial to ensuring the security, reliability, and sustainability of solar energy resources. Recommendations included pursuing adaptation strategies and technological innovations, such as integrating solar energy with other sources, while also expanding feed-in tariff incentives and policy interventions that promote sustainable solar energy utilization and further research. By implementing these measures, Kenya can enhance the long-term viability and resilience of solar energy in the face of a changing climate.
Efficacy of intraoperative subanesthetic dose of ketamine/esketamine in preventing postoperative cognitive dysfunction: a systematic review and meta-analysis

Background: Postoperative cognitive dysfunction (POCD) is a common complication after anesthesia surgery, especially in older people, that can persist weeks or months after surgery as a short-term impairment of cognitive abilities, or even for a prolonged duration over years, potentially progressing into permanent cognitive dysfunction. However, the pathogenesis of POCD is not fully understood, and therefore an optimal solution for preventing POCD has yet to be established. Some studies have shown that intraoperative ketamine/esketamine reduces the incidence of POCD, but this remains controversial. Objectives: We evaluated the effect of intraoperative subanesthetic doses of ketamine/esketamine versus no intervention in adults undergoing general anesthesia surgery on the incidence of POCD. Data Sources: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and searched the PubMed, Embase, Ovid, Cochrane, Scopus, and Web of Science databases for the MeSH terms ‘ketamine’, ‘esketamine’, and ‘Postoperative Cognitive Complications’ from database inception to 25 June 2023. Results: We found no statistically significant difference in the incidence of POCD within 7 days for intraoperative subanesthetic dose of ketamine/esketamine compared with the control group [relative risk (RR) = 0.57, 95% confidence interval (CI): 0.32, 1.01], and the results from the subgroup analysis based on age (>60 years) also revealed that the difference was not statistically significant (RR = 0.49, 95% CI: 0.23, 1.04). Conclusion: Compared with controls, intraoperative subanesthetic dose of ketamine/esketamine has no advantage in preventing POCD in patients, or in elderly patients. This study provides reference data for POCD research and clinical drug intervention strategies. Registration: Prospective Register of Systematic Reviews (PROSPERO; registration number CRD42023401159).
Improved Resistive and Synaptic Characteristics in Neuromorphic Systems Achieved Using the Double-Forming Process

In this study, we investigate the electrical properties of ITO/ZrOx/TaN RRAM devices for neuromorphic computing applications. The thickness and material composition of the device are analyzed using transmission electron microscopy. Additionally, the existence of TaON interface layers was confirmed using dispersive X-ray spectroscopy and X-ray photoelectron analysis. The forming process of the ZrOx-based device can be divided into two categories, namely single- and double forming, based on the initial lattice oxygen vacancies. The resistive switching behaviors of the two forming methods are compared in terms of the uniformity properties of endurance and retention. The rationale behind each I–V forming process was determined as follows: in the double-forming method case, an energy band diagram was constructed using F-N tunneling; conversely, in the single-forming method case, the ratio of oxygen vacancies was extracted based on XPS analysis to identify the conditions for filament formation. Subsequently, synaptic simulations for the applications of neuromorphic systems were conducted using a pulse scheme to achieve potentiation and depression with a deep neural network-based pattern recognition system to display the achieved recognition accuracy. Finally, high-order synaptic plasticity (spike-timing-dependent plasticity (STDP)) is emulated based on the Hebbian rule.
Topological speckles

The time evolution of a topological Su-Schrieffer-Heeger chain is analyzed through the statistics of speckle patterns. The emergence of topological edge states dramatically affects the dynamical fluctuations of the wavefunction. The intensity statistics is found to be described by a family of noncentral chi-squared distributions, with the noncentrality parameter reflecting on the degree of edge-state localization. The response of the speckle contrast with respect to the dimerization of the chain is explored in detail as well as the role of chiral symmetry-breaking disorder, number of edge states, their energy gap, and the locations between which the transport occurs. In addition to providing a venue for speckle customization, our results appeal to the use of speckle patterns for characterization of nontrivial topological properties.
The SourceData-NLP dataset: integrating curation into scientific publishing for training large language models

Introduction: The scientific publishing landscape is expanding rapidly, creating challenges for researchers to stay up-to-date with the evolution of the literature. Natural Language Processing (NLP) has emerged as a potent approach to automating knowledge extraction from this vast amount of publications and preprints. Tasks such as Named-Entity Recognition (NER) and Named-Entity Linking (NEL), in conjunction with context-dependent semantic interpretation, offer promising and complementary approaches to extracting structured information and revealing key concepts. Results: We present the SourceData-NLP dataset produced through the routine curation of papers during the publication process. A unique feature of this dataset is its emphasis on the annotation of bioentities in figure legends. We annotate eight classes of biomedical entities (small molecules, gene products, subcellular components, cell lines, cell types, tissues, organisms, and diseases), their role in the experimental design, and the nature of the experimental method as an additional class. SourceData-NLP contains more than 620,000 annotated biomedical entities, curated from 18,689 figures in 3,223 papers in molecular and cell biology. We illustrate the dataset's usefulness by assessing BioLinkBERT and PubmedBERT, two transformers-based models, fine-tuned on the SourceData-NLP dataset for NER. We also introduce a novel context-dependent semantic task that infers whether an entity is the target of a controlled intervention or the object of measurement. Conclusions: SourceData-NLP's scale highlights the value of integrating curation into publishing. Models trained with SourceData-NLP will furthermore enable the development of tools able to extract causal hypotheses from the literature and assemble them into knowledge graphs.
Recognition of masked and unmasked facial expressions in males and females and relations with mental wellness

Background While the effects of mask wearing/facial occlusion are known to impair facial expression recognition, little is known about the role of mental wellness on facial expression recognition, as well as the influence of sex on misattribution errors (i.e., confusions between emotions). In this large study, we aimed to address the relation between facial expression recognition and loneliness, perceived stress, anxiety, and depression symptoms in male and female adults. Methods We assessed the influence of mask-wearing on facial expression recognition [i.e., accuracy and response time (RT)] via an online study in N = 469 adult males and females across Canada. Results Expectedly, recognition was impaired under masked conditions (i.e., lower accuracy, longer RTs, more misattribution errors). Females were faster and more accurate than males, with less misattribution errors. A novel finding was that people with higher perceived stress were less accurate at identifying masked fearful faces. Perceived stress influenced the relation between sex and RT to masked happy faces; males with high stress scores were slower to recognize masked happy faces, the opposite was true for females. Finally, this study was among the first to show that higher loneliness predicted shorter RT to unmasked faces. Impact Our results show that facial expression recognition is impaired by mask-wearing, and that sex and mental health features are important predictors of performance. Such insight could be detrimental in certain sectors of the population (e.g., health care or education), and inform policies being adopted in future pandemics.
Scaling-robust built-in a posteriori error estimation for discontinuous least-squares finite element methods

A convincing feature of least-squares finite element methods is the built-in a posteriori error estimator for any conforming discretization. In order to generalize this property to discontinuous finite element ansatz functions, this paper introduces a least-squares principle on piecewise Sobolev functions for the solution of the Poisson model problem in 2D with mixed boundary conditions. It allows for fairly general discretizations including standard piecewise polynomial ansatz spaces on triangular and polygonal meshes. The presented scheme enforces the interelement continuity of the piecewise polynomials by additional least-squares residuals. A side condition on the normal jumps of the flux variable requires a vanishing integral mean and enables a natural weighting of the jump in the least-squares functional in terms of the mesh size. This avoids over-penalization with additional regularity assumptions on the exact solution as usually present in the literature on discontinuous LSFEM. The proof of the built-in a posteriori error estimation for the over-penalized scheme is presented as well. All results in this paper are robust with respect to the size of the domain guaranteed by a suitable weighting of the residuals in the least-squares functional. Numerical experiments exhibit optimal convergence rates of the adaptive mesh-refining algorithm for various polynomial degrees.
Sorting and Decentralized Price Competition ∗

We investigate under which conditions price competition in a market with matching frictions leads to sorting of buyers and sellers. Positive assortative matching obtains only if there is a high enough degree of complementarity between buyer and seller types. The relevant condition is rootsupermodularity ; i.e., the square root of the match value function is supermodular. It is a necessary and sufficient condition for positive assortative matching for any distribution of buyer and seller types, and does not depend on the details of the underlying matching function that describes the search process. The condition is weaker than log-supermodularity, a condition required for positive assortative matching in markets with random search. This highlights the role prices play in matching heterogeneous agents. Negative assortative matching obtains whenever the match value function is weakly submodular.
Prediction of Running Performances for World Class, Elite, Sub-elite, and Collegiate Athletes based on Maximal Aerobic Speed and Running Energy Reserve Index

Prediction of performance is an important parameter in exercise, especially in sports. There are several ways to predict performance, however many are fraught with difficulties such as error of prediction being too large or the lack of reliability and validity [1]. The recently published Maximal Aerobic Speed (MAS) validation and the Running Energy Reserve Index (RERI) had lower errors in predicting world class, elite, sub elite and collegiate performances [1,2]. The use of MAS and Maximal Anaerobic Speed to predict performance has been used previously by Bundle et al [3]. However
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r. t. accuracy, diversity, computational cost, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel model that leverages Gaussian process regression for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.