Miracle-dz/UniFAR-FTUs huggingface dataset:
AN IMPROVED RESULT ON TRIPLE NON-NEGATIVE SOLUTIONS FOR A CLASS OF HIGHER-ORDER FRACTIONAL DIFFERENTIAL EQUATIONS

This paper investigates the existence of triple non-negative solutions for a kind of higher- order nonlinear fractional differential equations by using Leggett-Williams fixed point theorem, and presents a new existence criterion. The fractional derivative here is the standard Riemann-Liouville one. A new approximation of the Green's function is derived to facilitate the proof of the main results. The study of an illustrative example shows that the new existence criterion obtained in this paper improves the existing results to some extent.
Eigenvalues of Large Dimensional Random Matrices

This paper demonstrates an introduction to the statistical distribution of eigenval-ues in Random Matrix theory. Using mathematical analysis and probabilistic measuretheory instead of statistical methods, we are able to draw conclusions on large dimen-sional cases and as our dimensions of the random matrices tend to innity. Applicationsof large-dimensional random matrices occur in the study of heavy-nuclei atoms, whereEigenvalues express some physical measurement or observation at a distinct state ofa quantum-mechanical system. This specically motivates our study of Wigner Ma-trices. Classical limit theorems from statistics can fail in the large-dimensional caseof a covariance matrix. By using methods from combinatorics and complex analysis,we are able to draw multiple conclusions on its spectral distributions. The Spectraldistributions that arise allow for boundedness to occur on extreme eigenvalues.
Processes on the unitary group and free probability

This thesis focuses on the asymptotic of objects related to the Brownian motion on the unitary group in large dimension, and on the study, in free probability, of the non-commutative versions of those objects. It subdivides into essentially three parts.In Chapter 2, we solve the original problem of this thesis: the convergence of the Hall transform on the unitary group to the free Hall transform, as the dimension tends to infinity. To solve this problem, we establish theorems of existence of transition kernel for the free convolution. Finally, we use these results to prove that, exactly as the Brownian motion on the unitary group, the Brownian motion on the linear group converges in noncommutative distribution to its free version. Then we study the fluctuations around this convergence in Chapter 3. Chapter 4 presents a homomorphism between infinitely divisible measures for the free convolution, in respectively the additive case and the multiplicative case. We show that this homomorphism has a matricialversion which is based on a new model of random matrices for the free multiplicative Levy processes.
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of “emotion,” coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a “Holy Grail” research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

—Prompt engineering is a technique that involves augmenting a large pre-trained model with task-speciﬁc hints, known as prompts, to adapt the model to new tasks. Prompts can be created manually as natural language instructions or generated automatically as either natural language instructions or vector representations. Prompt engineering enables the ability to perform predictions based solely on prompts without updating model parameters, and the easier application of large pre-trained models in real-world tasks. In past years, Prompt engineering has been well-studied in natural language processing. Recently, it has also been intensively studied in vision-language modeling. However, there is currently a lack of a systematic overview of prompt engineering on pre-trained vision-language models. This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models ( e.g., Flamingo), image-text matching models ( e.g., CLIP), and text-to-image generation models ( e.g., Stable Diffusion). For each type of model, a brief model summary, prompting methods, prompting-based applications, and the corresponding responsibility and integrity issues are summarized and discussed. Furthermore, the commonalities and differences between prompting on vision-language models, language models, and vision models are also discussed. The challenges, future directions, and research opportunities are summarized to foster future research on this topic.
Flagella are an important virulence factor in the subclinical persistence of Escherichia coli in bovine mammary gland

Abstract We compared the virulence profile and REP-PCR genotypes of Escherichia coli strains isolated from subclinical and clinical mastitis cases and dairy farm environments in Minas Gerais State, Brazil, to determine virulence factors and genotypes potentially associated with subclinical persistence in the udder. The virulence profile was obtained by the search for three virulence genes: lpfA (long polar fimbriae), fliC (flagella), and escN (type III secretion system). Subclinical isolates exhibited mainly the fliC gene (33.33%) and fliC + escN genes (30.30%). Clinical isolates exhibited mainly fliC + escN genes (50%) and environmental isolates the lpfA + escN genes (58.04%). Strains isolated from subclinical mastitis showed 6.75 times more positivity to fliC than environmental isolates. Thirty-four genotypes were observed in the REP-PCR analysis, and clinical mastitis isolates indicated more genetic proximity to dairy farm environment isolates than subclinical mastitis isolates. In conclusion, the results suggested that flagella may be an important virulence factor for mammary persistent E. coli infection in cattle, however, none of the E. coli REP-PCR genotypes were associated with subclinical infection.
Seasonal, physiological and bacteriological risk factors for subclinical mastitis in dairy cows maintained under different farming conditions

Abstract Subclinical mastitis (SCM) is a major health problem of dairy animals in India and across the globe. An identification of potential risk factors of SCM can help for efficient udder health management in dairy animals. In this study, apparently healthy cows (HF crossbred: n = 45; Deoni: n = 43) were screened for SCM during different seasons through milk somatic cell count (SCC: reference test using 200 × 103 cells/ml as cut off value), California mastitis test (CMT) and differential electrical conductivity (DEC) test at an organized research farm. SCM positive milk samples (n = 34) were inoculated in selective media for Coliform sp. , Streptococcus sp. and Staphylococcus sp. and DNA was isolated (n = 10) for species confirmation by 16s rRNA method. Both bivariate and multivariate models were used for risk assessment. We found the cumulative prevalence of 31 and 65% SCM in Deoni and crossbred cows, respectively. Screening of 328 crossbred cows under field conditions revealed point prevalence of 55% SCM. Multivariate analysis revealed stage of lactation (SOL), milk yield in previous lactation and test day milk yield in Deoni cows, as well as parity and mastitis treatment history in current lactation in HF crossbred cows as risk factors. SOL was a significant factor under field conditions. Receiver operated characteristic curve analysis revealed better accuracy of CMT than DEC. We found more mixed infections due to Staphylococcus sp. and Streptococcus sp. in culture, while 16s rRNA based molecular method revealed lesser-known pathogens associated with SCM. It is concluded that SCM prevalence rate is higher in crossbred than indigenous cows and these breeds have different risk factors for SCM. HF crossbred cows had similar SCM prevalence rate under different farming conditions, where CMT can be used for SCM diagnosis with excellent accuracy. The 16s rRNA method is useful for specific identification of lesser known and emerging mastitis pathogens.
Neural network–based reconfiguration control for spacecraft formation in obstacle environments

This paper proposes an adaptive formation reconfiguration control scheme based on the leader‐follower strategy for multiple spacecraft systems. By taking the predesigned desired velocities and the trajectories as reference signals, a set of coordination tracking controllers is constructed by combining the reconstructed dynamic system and the neural network–based reconfiguration algorithm together. To avoid collisions between spacecraft and obstacles during the formation configuration process, the null space–based behavioral control is integrated into the control design. Since the spacecraft dynamics contains unknown nonlinearity and disturbance, it is challenging to make the system robust to uncertainties and improve the control precision simultaneously. To solve this problem, both the adaptive neural network strategy and the finite‐time control theory are employed. Finally, 2 simulation examples are carried out to verify the proposed algorithm, showing that the formation reconfiguration task can be executed successfully while achieving high control precision.
Investigating possible regional dependence in strong ground motions

It is common practice to use ground-motion models, often developed by regression on recorded accelerograms, to predict the expected earthquake ground motions at sites of interest. An important consideration when selecting these models is the possible dependence of ground motions on geographical region, i.e., are median ground motions in the (target) region of interest for a given magnitude and distance the same as those in the (host) region where a ground-motion model is from, and are the aleatory variabilities of ground motions also similar? In this brief article, some of the recent literature with relevance to these questions is summarized. It is concluded that although some regions seem to show considerable differences in shaking it is currently more defensible to use well-constrained models, possibly based on data from other regions, rather than use local, often poorly-constrained, models. In addition, it is noted that the presence of ‘pseudo-regional dependency’ due to differences in, for example, focal depths, average shear-wave velocity profiles or focal mechanisms can lead to apparent variations between areas when these variations could be captured in well-characterized ground-motion prediction equations.
Do we play or gamify? Evaluation of gamification training experience to improve the digital competence of university teaching staff

Over the last few decades, studies have been carried out in the field of education that endorse the benefits that gamification offers both for teachers and students. The university is working along these lines, with the aim of developing optimal skills that benefit students, both personally and professionally. Consequently, this training designed and selected for teachers is essential to achieve a successful gamified experience. The main purpose of this research is to present a training experience carried out in a university in Latin America and to verify the perception of this methodological strategy by this educational institution. In other words, the aim is to demonstrate whether gamification is perceived as useful by university teaching staff. To this end, two designs were used: descriptive and validation by means of structural equations (PLS), in order to evaluate the degree of acceptance of gamification as an active methodology based on the Technology Acceptance Model (TAM). The results show that the proposal is suitable for university teaching. All the participants, 114, perceived this strategy as adequate, detailing the dimensions related to the ease of use in the classroom, its integration, the transformation of interest in learning, and the ability to show a positive attitude in its use. At the same time, the level of acceptance regarding the validation of the TAM is high. Finally, the assessment of the replicability of the model in future and similar research, or through so-called emerging technologies, is detailed.
Pepperdine Digital Commons Pepperdine Digital Commons

: Accurate measurement of digital divides is important for policy purposes. Empirical studies on broadband subscription gaps have largely used cross-sectional data, which cannot speak to the timing of technological adoption. Yet, the dynamics of a digital divide are important and deserve study. With the goal of improving our understanding of appropriate techniques for analyzing digital divides, we review competing econometric methodology and propose the use of duration analysis. We compare the performance of alternative estimation methods using a large dataset on DSL subscription in the U.S., paying particular attention to whether women, blacks, and Hispanics catch up to others in the broadband adoption race. We conclude that duration analysis best captures the dynamics of the broadband gaps and can be a useful addition to the analytic tool box of digital divide researchers. Our results support the official collection of broadband statistics in panel form, where the same households are followed over time.
Curry, Nigel and Kirwan, James R (2014) The Role of Tacit Knowledge in Developing Networks for Sustainable Agriculture. Sociologia Ruralis, 54 (3),

Whilst objectivist epistemologies have been dominant in productivist agriculture, the local, cultural and environmental contexts of sustainable agriculture are more fully informed by constructivist epistemologies. Within constructivism, tacit knowledge – an intuitive knowledge that cannot be formalised – is explored empirically. Six types of tacit knowledge were identified as a result of working closely with a sustainable food network: the Brighton and Hove Food Partnership. Customs cohere around integrating food with other sustainable activity; developing a complex unregulated organisation requires savoire faire . The unique character of Brighton has developed an operational folklore , and network identity is important, particularly in relation to conventional agriculture and to the city as a whole. A confidence in people’s roles has helped define network development and using different discourses, communicating the network in diverse contexts, is important for understanding the network. All these tacit knowledge elements have a strong influence over the network but have to be assimilated rather than learned.
FIELD AND NUMERICAL STUDY OF THE WIND-WAVE REGIME ON THE GORKY RESERVOIR

The paper describes the study of wind-wave regime at the Gorky reservoir. A series of field experiments (carried out from May to October in 2012–2015) showed that the values of the drag coefficient CD for a middle-sized reservoir in the range of moderate and strong winds are approximately 50 % lower than its values typical of the ocean conditions. The obtained parameterization of CD was implemented in the wave model WAVEWATCH III to receive the correct wave forecasts for a middle-sized reservoir. Statistical distribution of the wind speeds and directions called for consideration of wind field heterogeneity over the Gorky reservoir. It was incorporated using the wind forcing from atmospheric model WRF to WAVEWATCH III. Homogeneous wind forcing from the experimental data was compared with heterogeneous wind forcing from WRF. The need for further improvement of the quality of wind and wave prediction is discussed.
The Chagga people and environmental changes on Mount Kilimanjaro: Lessons to learn

Mount Kilimanjaro, the highest mountain in Africa, is a site of exceptional diversity. However, its fragile balance is presently under threat, mainly from hydrological issues. Scientists and politicians have different hypotheses concerning the condition and availability of water on the mountain, thus far without consensus. Some of these hypotheses infer that the local Chagga communities living on Kilimanjaro’s slopes are implicated in the overall degradation of natural resources. A major part of the research presented in this paper focuses on the Chagga people’s view of their environment. The research uses the Actor in 4 Dimensions (A4D) methodology. This multidisciplinary conceptual model helps one to understand a territory by analysing the relations between individuals (social link) and the relations between humans and no humans (natural link). This paper aims to (1) present the A4D model and its innovative methodology, (2) analyse the stakeholders’ dynamics around environmental issues on the mountain and (3) focus on the Chagga’s perception of natural resource evolution and management. The A4D results show that the Chagga display a profound attachment to their territory, the forest is the element creating social relations, technical progress can mean social regression, conflicts over natural resources are increasing and hydrological risks on Mount Kilimanjaro are primarily of a social nature.
The Level of Job Performance among Arab School Teachers in the Northern Region of Israel from Teachers' Perspective

: This study aimed to identify the level of job performance among Arab school teachers in the Northern Region of Israel from the teachers' perspective and to figure out the impact of variables such as gender, educational qualification, years of experience, and educational stage on teachers' estimates of the level of job performance. The study sample consisted of 390 teachers, who were chosen by the simple random method, and the descriptive approach was implemented; a questionnaire was used to collect the necessary data to achieve the objectives of the study. The results revealed that the level of job performance of Arab school teachers from the teachers' perspective was high. The results further indicated that there were significant statistical differences at = 0.05 in the estimates of the study sample of the level of their job performance due to the variable "academic qualification" in favor of graduate studies. On the other hand, for the variable "educational stage" in the fields of follow - up and evaluation, classroom learning management and its activities, and the overall grade in favor of the preparatory stage, the results did not show any significant differences at the level of significance = 0.05 in the estimates of the study sample of the level of their job performance due to the variables "gender and seniority. "
Some Problems in Mathematical Genealogy

After a general review of symmetric reversibility for countable-state continuous-time Markov chains the author shows that the birth-death-and-immigration process is symmetrically reversible and further that it remains so even when the description of the present state is refined to include a list of the sizes of all ‘families’ alive at the epoch in question. This result can be useful in genealogy because the operational direction of time there is the negative one. In view of the symmetric reversibility, some of the questions which face the genealogist can be answered without further calculation by quoting known results for the process with the usual (‘forward’ instead of ‘backward’) direction of time. Further topics discussed include social mobility matrices, surname statistics, and Colin Rogers' ‘problem of the Spruces’.
Widespread Deviant Patterns of Heterozygosity in Whole-Genome Sequencing Due to Autopolyploidy, Repeated Elements, and Duplication

Most population genomic tools rely on accurate SNP calling and filtering to meet their underlying assumptions. However, genomic complexity, due to structural variants, paralogous sequences and repetitive elements, presents significant challenges in assembling contiguous reference genomes. Consequently, short-read resequencing studies can encounter mismapping issues, leading to SNPs that deviate from Mendelian expected patterns of heterozygosity and allelic ratio. In this study, we employed the ngsParalog software to identify such deviant SNPs in whole-genome sequencing data from four species: Arctic Char (Salvelinus alpinus), Lake Whitefish (Coregonus clupeaformis), Atlantic Salmon (Salmo salar), and the American Eel (Anguilla rostrata), with low (2X) to intermediate (6X) coverage. The analyses revealed that deviant SNPs accounted for up to 62% of all SNPs in salmonid datasets and approximately 10% in the American Eel dataset. These deviant SNPs were particularly concentrated within repetitive elements and genomic regions that had recently undergone rediploidization in salmonids. Additionally, narrow peaks of elevated coverage were ubiquitous along all four reference genomes, encompassed most deviant SNPs and could be partially attributed to transposons and tandem repeats. Including these deviant SNPs in genomic analyses led to highly distorted site frequency spectra, apparent homogenization of populations and underestimating pairwise FST values. Considering the widespread occurrence of deviant SNPs arising from a variety of source, their important impact in estimating population parameters, and the availability of effective tools to identify them, we propose that excluding deviant-SNPs from WGS datasets is required to improve genomic inferences for a wide range of taxa and sequencing depths. Significance: Genomes can be very repetitive and hard to assemble into a reference, which can lead to biases when genotyping genetic markers in complex genomic regions. Here, we draw attention to this issue in various whole-genome datasets and validate a method to identify problematic SNPs at low coverage. We also explore processes creating such SNPs and their consequences on common population genomics analyses.