Miracle-dz/UniFAR-FTUs huggingface dataset:
On Liapounoff's Conditions of Stability

1. In his well known paper, "Problbme general de la stabilite du mouvement" [3], Liapounoff has given several general criteria for the stability of a solution of a system of differential equations, independently of the consideration of the variational equations. These criteria have been further generalized by several Soviet mathematicians (see References at the end of this note). In the present paper, I introduce a new and more strict type of stability, equiasymptotic stability, whose relations to the asymptotic stability of Liapounoff are discussed (Theorem 7 and Example 4). I will show that the sufficient conditions assumed in a theorem of Liapounoff on asymptotic stability and in a theorem of Malkin on simple stability, imply even equiasymptotic stability (Theorem 5). On the other hand, I show that the above mentioned theorem of Liapounoff can be proved under weaker assumptions (Theorem 6). This weakening of the sufficient conditions is needed in order to prove the reciprocal theorem, which is not true, even in very simple cases, under the more restrictive conditions of Liapounoff. Necessary and sufficient conditions for asymptotic stability are thus found in the case of periodic (Theorem 8) or linear (Theorem 9) systems. 2. DEFINITIONS AND NOTATIONS. Consider a system
Effectiveness of BA.1- and BA.4/BA. 5-Containing Bivalent COVID-19 mRNA Vaccines Against Symptomatic SARS-CoV-2 Infection During the BA.5-Dominant Period in Japan

Abstract In this multicenter, prospective, test-negative, case-control study in Japan, the effectiveness of both BA.1-containing and BA.4/BA.5-containing bivalent coronavirus disease 2019 mRNA vaccines against symptomatic infection during the BA.5-dominant period was high compared with no vaccination (65% and 76%) and moderate compared with monovalent vaccines administered over half a year earlier (46% combined).
Enhancing Arabic‐text feature extraction utilizing label‐semantic augmentation in few/zero‐shot learning

A growing amount of research use pre‐trained language models to address few/zero‐shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develop a meta learner from the input texts solely. In this work, we demonstrate how label information can be utilized to extract enhanced feature representation of the input text from a Transformer‐based pre‐trained language model such as AraBERT. In addition, how this approach can improve performance when the data resources are scarce like in the Arabic language and the input text is short with little semantic information as is the case using tweets. The work also applies zero‐shot text classification to predict new classes with no training examples across different domains including sarcasm detection and sentiment analysis using the information in the last layer of a trained classifier in a transfer learning setting. Experiments show that our approach has a better performance for the few‐shot sentiment classification compared to baseline models and models trained without augmenting label information. Moreover, the zero‐shot implementation achieved an accuracy up to 0.874 in Arabic sarcasm detection from a model trained on a sentiment analysis task.
Do fiscal deficits influence private savings in South Africa? An empirical note

Orientation: The neoclassical loanable funds theory predicts that fiscal deficits reduce the pool of available savings in the economy, but heterodox scholarship disputes the claim.Research purpose: The paper sets out to test the relationship between fiscal deficits and private savings.Motivation for the study: Scholars are divided on whether corporate South Africa is building up excess savings, with some arguing that it is not and others arguing that it is because of mistrust of government and uncertainty largely. The trends of net private savings and fiscal deficits suggest that something contrary to the postulation of the neoclassical loanable funds theory could be happening in the South African context.Research approach/design and method: The paper used data for the period 1960–2021 within the Toda-Yamamoto vector autoregressive modelling framework.Main findings: The results show that fiscal deficits increase savings but reduce interest rates in line with the heterodox view that the financial asset creation effect of debt-financed deficits improves savings and reduces interests through the liquidity channel.Managerial/practical implications: The lesson for policy is that fiscal deficits are healthy for the economy. However, the government must manage them at some reasonable level to maintain a constant flow of net financial assets to the private sector, thus bolstering savings. An unhealthy pursuit of fiscal surpluses can shift deficits to households and firms, and breed systemic financial instability.Contribution/value-added: The paper contributed to a nuanced understanding of the role of fiscal deficits in expanding savings, which runs counter to the theoretical underpinnings of fiscal conduct that pursues budget surpluses.
Assessment of Doable Technologies for enhancing the yield potentials in Sunflower (Helianthus annuus L.) under Rainfed Conditions

Cluster FLDs were organized by AICRP on sunflower scheme, Regional Agricultural Research Station, Nandyal during three consecutive years of 2018-19, 2019-20 and 2020-21 in an area of 50 ha at 66 locations. At every demonstration site improved management practices viz improved sunflower hybrid with high oil content (Prabhath), thinning at 10 days after sowing boron spray @0.2% at ray floret stage and need based plant protection measures were demonstrated against viz., farmer’s practice of sunflower cultivation. The sunflower yields under demonstration practice higher than farmer practice in all the three years. The percentage increase over farmers practice was lowest 13.3% during 2020-21 and highest 25.6 during 2018- 19 with an average increase of 20.7 per cent observed in demonstration practice. The average gross returns of three years were Rs. 92477/- per hectare in demo practice which is 19.7 per cent higher than the farmers practice (Rs. 72262/- per ha.). The net returns of Rs.65393/- per hectare was recorded under demonstration practice and it was 26.9 per cent higher than the farmer practice (Rs. 51528/- per ha.). The mean benefit cost ratio of three years of demonstration practice higher (1:3.2) than farmer practice (1:2.96).
A Swiss Army formula of Palm calculus

We obtain a single formula which, when its components are adequately chosen, transforms itself into the main formulas of the Palm theory of point processes: Little's L = λW formula [10], Brumelle's H = λG formula [5], Neveu's exchange formula [14], Palm inversion formula and Miyazawa's rate conservation law [12]. It also contains various extensions of the above formulas and some new ones.
Conservation Innovations and Future Directions for the Study of Rhinoceros Gut Microbiome

Rhinoceros are among the largest and most endangered herbivores in the world. Three of the five extant species are critically endangered, with poaching and habitat loss contributing heavily to declines. The gut microbiome is an essential facet of host health and digestion, mediating a variety of immune and physiological functions. Certain microbes have the potential to serve as biomarkers for reproductive outcomes and predictors of disease susceptibility. Therefore, assessing gut microbial dynamics in relation to wild and managed rhinoceros populations has particular relevance for zoos and other conservation organizations that maintain assurance populations of these charismatic megafauna. The functional gut microbiomes associated with all rhinoceros species remain poorly studied, and many published reports are limited by small sample sizes and sex biases. In this review, we synthesize current research to examine the rhinoceros gut microbiome under human management and resulting conservation implications, address common pitfalls of wildlife gut microbiome studies, and propose future avenues of research in this field.
Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. We combine nonequilibrium molecular dynamics simulations with two unsupervised machine learning methods: principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), to extract the correlation between the rheological properties and molecular structure of squalane sheared at high strain rates (106–1010s−1) for which substantial shear thinning is observed under pressures P∈0.1–955 MPa at 293 K. Intramolecular atom pair orientation tensors of 435×6 dimensions and the intermolecular atom pair orientation tensors of 61×6 dimensions are reduced and visualized using PCA and t-SNE to assess the changes in the orientation order during the shear thinning of squalane. Dimension reduction of intramolecular orientation tensors at low pressures P=0.1,100 MPa reveals a strong correlation between changes in strain rate and the orientation of the side-backbone atom pairs, end-backbone atom pairs, short backbone-backbone atom pairs, and long backbone-backbone atom pairs associated with a squalane molecule. At high pressures P≥400 MPa, the orientation tensors are better classified by these different pair types rather than strain rate, signaling an overall limited evolution of intramolecular orientation with changes in strain rate. Dimension reduction also finds no clear evidence of the link between shear thinning at high pressures and changes in the intermolecular orientation. The alignment of squalane molecules is found to be saturated over the entire range of rates during which squalane exhibits substantial shear thinning at high pressures.
Distribution of thornless Mimosa tenuiflora, Piptadenia stipulacea and Cnidoscolus quercifolius in a seasonally dry tropical forest remnant

ABSTRACT The density and distribution of three tree species of local economic importance with, but sometimes without, thorns (Mimosa tenuiflora, Piptadenia stipulacea) or urticating hairs (Cnidoscolus quercifolius) thriving in native seasonally dry tropical forests, were assessed in a 43.5-hectare cattle-grazed caatinga remnant, located in Patos/Paraíba, Brazil. We recorded and georeferenced all the thorny and thornless regenerant plants (≥50 cm high and circumference at breast height (CBH) < 6 cm). We also recorded and georeferenced all the mature thorny and thornless trees (CBH ≥ 6 cm); in addition all the thornless mature trees and their respective nearest thorny mature plant were measured. Thornless M. tenuiflora, P. stipulacea and C. quercifolius totaled 560, 9 and 13 plants, respectively. The thornless plants formed groups and the mature ones showed height and CBH similar to their respective nearest mature thorny neighbor. Based on our data and on published data, we suggest management practices aiming at replacing thorny trees by thornless trees, including among others: the planting of 15 groups/ha of, respectively, 7, 2 and 2 thornless M. tenuiflora, P. stipulacea and C. quercifolius seedlings. Such a progressive replacement of the thorny trees by thornless trees would facilitate grazing and the management of the forest.
Perception changes over time on the usefulness of language learning strategies

This longitudinal study describes and analyses the shifts in students’ perceptions on the usefulness of language learning strategies (LLS) when learning Spanish as a foreign language, and explains the roles that these learning strategies play at various moments in time during students’ language learning journey at the undergraduate level. The richness of this investigation lies in the examination of the changing roles and the perceived usefulness of these strategies over a period of 3 years by using a mixed-method approach. Its main contribution is twofold. On the one hand, it offers practical insights within the linguistic field of language acquisition that can be applied to the learning of foreign languages by students with no previous knowledge of them. On the other hand, its longitudinal design not only goes beyond the traditional quantitative approach that offers a limited snapshot view, but also captures changes in strategy use over time while providing details on why strategies were or were not used in the new language at different proficiency levels. The study found some strategies that were initially perceived as useful and helpful at the beginning of the journey were later perceived as neutral, useless or obstructive by the same group of students who had become more proficient in the foreign language. And, conversely, other strategies that were initially perceived as neutral, unhelpful or frustrating, were later perceived as necessary to improve language proficiency, useful and stimulating by the same cohort of students. In this respect, it was found that the perceived role played by these strategies alternated between essential roles to catalytic roles, imperceptible roles, retarding roles and preventing roles, and vice versa.Contribution: The main contribution of this study is that it shows that students’ perceptions on the usefulness of certain LLS over time are influenced by their level of proficiency. This in turn influences, and is influenced by, language attitudes and language learning policy, which fall under the scope of this journal.
QMRNB: Design of an Efficient Q-Learning Model to Improve Routing Efficiency of UAV Networks via Bioinspired Optimizations

– The design of efficient routing strategies for Unmanned Aerial Vehicle (UAV) Networks is a multidomain task that involves analysis of node-level & network-level parameters, and mapping them with communication & contextual conditions. Existing path planning optimization models either showcase higher complexity or cannot be scaled for larger network scenarios. Moreover, the efficiency of these models also reduces w.r. t. the number of communication requests, which limits their scalability levels. To get a better result over these challenges, this article provides an idea to design an efficient Q-Learning model to improve the routing efficiency of UAV networks via bioinspired optimizations. The model initially collects temporal routing performance data samples for individual nodes and uses them to form coarse routes via Q-Learning optimizations. These routes are further processed via a Mayfly Optimization (MO) Model, which assists in the selection of optimal routing paths for high Quality of Service (QoS) even under large-scale routing requests. The MO Model can identify alternate paths via the evaluation of a high-density routing fitness function that assists the router in case the selected paths are occupied during current routing requests. This assists in improving temporal routing performance even under dense network conditions. Due to these optimizations, the model is capable of reducing the routing delay by 8.5%, improving energy efficiency by 4.9%, and reducing the routing jitter by 3.5% when compared with existing routing techniques by taking similar routing conditions.
Oral hygiene awareness and practice in orthodontic patients in Makkah city: A cross sectional study

OBJECTIVES: To evaluate the level of oral hygiene awareness and practice among orthodontic patients that have fixed appliances and investigate the differences in awareness as per age, gender, and education qualification. MATERIALS AND METHODS: A cross-sectional study with 250 patients was included. A questionnaire was used to collect information regarding the patient's oral hygiene. RESULTS: The level of awareness and oral hygiene practice was significantly higher among females, with a consistent stepwise increase toward older ages. Also, increased with higher education level, longer duration since getting the appliance, and receiving instructions from the orthodontist. CONCLUSIONS: Oral hygiene instructions received by the orthodontist play an important role in the patient's awareness level. Also, many predictors have a significant effect on the patient's cooperation in practicing oral hygiene measures.
Mirror, Mirror on the Wall: Algorithmic Assessments, Transparency, and Self-Fulfilling Prophecies

Predictive algorithmic scores can significantly impact the lives of assessed individuals by shaping decisions of organizations and institutions that affect them, for example, influencing the hiring prospects of job applicants or the release of defendants on bail. To better protect people and provide them the opportunity to appeal their algorithmic assessments, data privacy advocates and regulators increasingly push for disclosing the scores and their use in decision-making processes to scored individuals. Although inherently important, the response of scored individuals to such algorithmic transparency is understudied. Inspired by psychological and economic theories of information processing, we aim to fill this gap. We conducted a comprehensive empirical study to explore how and why disclosing the use of algorithmic scoring processes to (involuntarily) scored individuals affects their behaviors. Our results provide strong evidence that the disclosure of fundamentally erroneous algorithmic scores evokes self-fulfilling prophecies that endogenously steer the behavior of scored individuals toward their assessment, enabling algorithms to help produce the world they predict. Our results emphasize that isolated transparency measures can have considerable side effects with noticeable implications for the development of automation bias, the occurrence of feedback loops, and the design of transparency regulations.
AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification

Convolutional models have provided outstanding performance in the analysis of hyperspectral images (HSIs). These architectures are carefully designed to extract intricate information from nonlinear features for classification tasks. Notwithstanding their results, model architectures are manually engineered and further optimized for generalized feature extraction. In general terms, deep architectures are time-consuming for complex scenarios, since they require fine-tuning. Neural architecture search (NAS) has emerged as a suitable approach to tackle this shortcoming. In parallel, modern attention-based methods have boosted the recognition of sophisticated features. The search for optimal neural architectures combined with attention procedures motivates the development of this work. This article develops a new method to automatically design and optimize convolutional neural networks (CNNs) for HSI classification using channel-based attention mechanisms. Specifically, 1-D and spectral–spatial (3-D) classifiers are considered to handle the large amount of information contained in HSIs from different perspectives. Furthermore, the proposed automatic attention-based CNN (AAtt-CNN) method meets the requirement to lower the large computational overheads associated with architectural search. It is compared with current state-of-the-art (SOTA) classifiers. Our experiments, conducted using a wide range of HSI images, demonstrate that AAtt-CNN succeeds in finding optimal architectures for classification, leading to SOTA results.
Long-Term Safety and Efficacy of Pars Plana Vitrectomy for Uveitis: Experience of a Tertiary Referral Centre in the United Kingdom

Aim: To evaluate the effectiveness of pars plana vitrectomy (PPV) without macular intervention on uveitis eyes with persistent vitreous inflammation/opacities in terms of visual acuity (VA), intraocular inflammation and macular profile. Methods: We carried out a single-center retrospective study of patients with uveitic eyes that underwent PPV without intervention on the macula due to persistent vitreous inflammation/opacities. The primary outcome measures were best-corrected visual acuity (BCVA), intraocular inflammation and macular profile at 3, 12 and 24 months after surgery. Results: Twenty-seven eyes of twenty-six patients were analyzed. Overall, 77.8% had an improvement of VA (55% by 0.3 LogMAR or more); 62.5% of patients had no intraocular inflammation, and the number of patients on systemic steroids and second-line immunosuppressives was reduced by 26% at 12 months; 87.5% of patients had resolution of macular oedema at 12 months. Conclusion: PPV for persistent vitreous inflammation/opacities is safe and effective, showing beneficial outcomes in terms of improvement of BCVA and the reduction in inflammation.
Transcranial Electrical Stimulation for Associative Memory Enhancement: State-of-the-Art from Basic to Clinical Research

Associative memory (AM) is the ability to bind new information into complex memory representations. Noninvasive brain stimulation (NIBS), especially transcranial electric stimulation (tES), has gained increased interest in research of associative memory (AM) and its impairments. To provide an overview of the current state of knowledge, we conducted a systematic review following PRISMA guidelines covering basic and clinical research. Out of 374 identified records, 41 studies were analyzed—twenty-nine in healthy young adults, six in the aging population, three comparing older and younger adults, as well as two studies on people with MCI, and one in people with Alzheimer’s dementia. Studies using transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS) as well as oscillatory (otDCS) and high-definition protocols (HD-tDCS, HD-tACS) have been included. The results showed methodological heterogeneity in terms of study design, stimulation type, and parameters, as well as outcome measures. Overall, the results show that tES is a promising method for AM enhancement, especially if the stimulation is applied over the parietal cortex and the effects are assessed in cued recall paradigms.
MYCN Amplification Is Associated with Reduced Expression of Genes Encoding γ-Secretase Complex and NOTCH Signaling Components in Neuroblastoma

Amplification of the MYCN oncogene is found in ~20% of neuroblastoma (NB) cases and correlates with high-risk disease and poor prognosis. Despite the plethora of studies describing the role of MYCN in NB, the exact molecular mechanisms underlying MYCN’s contribution to high-risk disease are not completely understood. Herein, we implemented an integrative approach combining publicly available RNA-Seq and MYCN ChIP-Seq datasets derived from human NB cell lines to define biological processes directly regulated by MYCN in NB. Our approach revealed that MYCN-amplified NB cell lines, when compared to non-MYCN-amplified cell lines, are characterized by reduced expression of genes involved in NOTCH receptor processing, axoneme assembly, and membrane protein proteolysis. More specifically, we found genes encoding members of the γ-secretase complex, which is known for its ability to liberate several intracellular signaling molecules from membrane-bound proteins such as NOTCH receptors, to be down-regulated in MYCN-amplified NB cell lines. Analysis of MYCN ChIP-Seq data revealed an enrichment of MYCN binding at the transcription start sites of genes encoding γ-secretase complex subunits. Notably, using publicly available gene expression data from NB primary tumors, we revealed that the expression of γ-secretase subunits encoding genes and other components of the NOTCH signaling pathway was also reduced in MYCN-amplified tumors and correlated with worse overall survival in NB patients. Genetic or pharmacological depletion of MYCN in NB cell lines induced the expression of γ-secretase genes and NOTCH-target genes. Chemical inhibition of γ-secretase activity dampened the expression of NOTCH-target genes upon MYCN depletion in NB cells. In conclusion, this study defines a set of MYCN-regulated pathways that are specific to MYCN-amplified NB tumors, and it suggests a novel role for MYCN in the suppression of genes of the γ-secretase complex, with an impact on the NOTCH-target gene expression in MYCN-amplified NB.